Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
Abstract
In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Positive results have been shown for bilateral filtering and Gaussian blur algorithms among image-processing algorithms.
“Of the image-processing algorithms, positive results have been shown for bilateral filtering and Gaussian blur algorithms [49,50].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [50]
Cites
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,143 · see citing works
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,417 · see citing works
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 3× · see citing works
“The ABF retains the general form of the bilateral filter [23] , but contains two important modifications.”
Fundamentals of digital image processing
Jain, A. K. (1988) · Cited by 7,000 · see citing works
(1982) · Elsevier eBooks · doi:10.1016/c2009-0-21955-6 · Cited by 4,628 · see citing works
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333 · see citing works
Gupta, R. P. (1991) · Springer eBooks · doi:10.1007/978-3-662-12914-2_12 · Cited by 1,430 · see citing works
Black, M. J., Sapiro, G., Marimont, D. H., & Heeger, D. J. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661192 · Cited by 1,294 · see citing works
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
Paris, S., & Durand, F. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0110-8 · Cited by 796 · see citing works
On the origin of the bilateral filter and ways to improve it
Elad, M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801126 · Cited by 795 · see citing works
Fleishman, S., Drori, I., & Cohen‐Or, D. (2003) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1201775.882368 · Cited by 626 · see citing works
A general framework for low level vision
Sochen, N., Kimmel, R., & Malladi, R. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661181 · Cited by 622 · see citing works
Barash, D. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1008390 · Cited by 514 · see citing works
Fleishman, S., Drori, I., & Cohen‐Or, D. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882368 · Cited by 496 · see citing works
Order statistics in digital image processing
Pitas, I., & Venetsanopoulos, A. N. (1992) · Proceedings of the IEEE · doi:10.1109/5.192071 · Cited by 476 · see citing works
Non-iterative, feature-preserving mesh smoothing
Jones, T. R., Durand, F., & Desbrun, M. (2003) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1201775.882367 · Cited by 424 · see citing works
Generalized median filtering and related nonlinear filtering techniques
Lee, Y., & Kassam, S. A. (1985) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1985.1164591 · Cited by 418 · see citing works
Real-time edge-aware image processing with the bilateral grid
Chen, J., Paris, S., & Durand, F. (2007) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1275808.1276506 · Cited by 402 · see citing works
Real-time edge-aware image processing with the bilateral grid
Chen, J., Paris, S., & Durand, F. (2007) · ACM Transactions on Graphics · doi:10.1145/1276377.1276506 · Cited by 394 · see citing works
Adaptive smoothing: a general tool for early vision
Saint-Marc, P., Chen, J. S., & Medioni, G. G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.87339 · Cited by 363 · see citing works
Iterative Identification and Restoration of Images
Lagendijk, R. L., Katsaggelos, A. K., & Biemond, J. (1991) · Springer eBooks · doi:10.1007/978-1-4615-3980-3 · Cited by 346 · see citing works
Non-iterative, feature-preserving mesh smoothing
Jones, T. R., Durand, F., & Desbrun, M. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882367 · Cited by 337 · see citing works
Detail-preserving ranked-order based filters for image processing
Arce, G. R., & Foster, R. E. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.17503 · Cited by 232 · see citing works
The staircasing effect in neighborhood filters and its solution
Buades, A., Coll, B., & Morel, J. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.871137 · Cited by 228 · see citing works
A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift
Barash, D., & Comaniciu, D. (2003) · Image and Vision Computing · doi:10.1016/j.imavis.2003.08.005 · Cited by 222 · see citing works
Separable Bilateral Filtering for Fast Video Preprocessing
Pham, T. Q., & van Vliet, L. J. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521458 · Cited by 219 · see citing works
A gentle introduction to bilateral filtering and its applications
Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2007) · ACM SIGGRAPH Courses · doi:10.1145/1281500.1281602 · Cited by 211 · see citing works
Retinex by Two Bilateral Filters
Elad, M. (2005) · Lecture notes in computer science · doi:10.1007/11408031_19 · Cited by 169 · see citing works
Optimal image scaling using pixel classification
Atkins, C. B., Bouman, C. A., & Allebach, J. P. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2001.958257 · Cited by 156 · see citing works
Self-citationAn overview of median and stack filtering
Gabbouj, M., Coyle, E. J., & Gallagher, N. C. (1992) · Circuits Systems and Signal Processing · doi:10.1007/bf01189220 · Cited by 124 · see citing works
Regularization in Image Restoration and Reconstruction
Karl, W. C. (2005) · Elsevier eBooks · doi:10.1016/b978-012119792-6/50075-9 · Cited by 113 · see citing works
Diffusions and Confusions in Signal and Image Processing
Sochen, N., Kimmel, R., & Bruckstein, A. M. (2001) · Journal of Mathematical Imaging and Vision · doi:10.1023/a:1011277827470 · Cited by 104 · see citing works
Theory of order statistic filters and their relationship to linear FIR filters
Longbotham, H. G., & Bovik, A. C. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.21690 · Cited by 87 · see citing works
Tone-dependent error diffusion
Li, P., & Allebach, J. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819232 · Cited by 77 · see citing works
Self-citationOptimal unsharp mask for image sharpening and noise removal
Kim, S. H., & Allebach, J. P. (2005) · Journal of Electronic Imaging · doi:10.1117/1.1924510 · Cited by 68 · see citing works
Self-citationAdaptive nonlinear filters for 2D and 3D image enhancement
Guillon, S., Baylou, P., Najim, M., & Keskes, N. (1998) · Signal Processing · doi:10.1016/s0165-1684(98)00042-5 · Cited by 55 · see citing works
Siddiqui, H., & Bouman, C. A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.888356 · Cited by 44 · see citing works
Nonlinear Filtering for Image Analysis and Enhancement
Arce, G. R., Bacca, J., & Paredes, J. L. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374457-9.00012-3 · Cited by 35 · see citing works
Novel bilateral filter approach: Image noise reduction with sharpening
Aleksic, M., Smirnov, M., & Goma, S. R. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.643880 · Cited by 32 · see citing works
AM/FM halftoning: digital halftoning through simultaneous modulation of dot size and dot density
He, Z., & Bouman, C. A. (2004) · Journal of Electronic Imaging · doi:10.1117/1.1669555 · Cited by 32 · see citing works
Inkjet printer model-based halftoning
Lee, J., & Allebach, J. P. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.843787 · Cited by 25 · see citing works
Self-citationAn investigation of perceived sharpness and sharpness metrics
Zhang, B., Allebach, J. P., & Pizlo, Z. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.584627 · Cited by 24 · see citing works
Self-citationImproving nonlinear up-scaling by adapting to the local edge orientation
Tegenbosch, J. A. P., Hofman, P. M., & Bosma, M. K. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.525714 · Cited by 24 · see citing works
Classification-based hybrid filters for image processing
Hu, H., & de Haan, G. E. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.642138 · Cited by 20 · see citing works
Design of Tone-Dependent Color-Error Diffusion Halftoning Systems
Monga, V., Damera-Venkata, N., & Evans, B. L. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884923 · Cited by 15 · see citing works
Quantization of accumulated diffused errors in error diffusion
Chang, T., & Allebach, J. P. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859372 · Cited by 15 · see citing works
Self-citationBilateral interpolation filters for image size conversion
Yang, S., & Hong, K. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530223 · Cited by 13 · see citing works
Sharpness Improvement Adaptive to Edge Strength of Color Image
Kotera, H., Yamada, Y., & Shimo, K. (2000) · Color and Imaging Conference · doi:10.2352/cic.2000.8.1.art00028 · Cited by 9 · see citing works
Hybrid order statistic filter and its application to image restoration
Thompson, E. A., Hardie, R. C., & Barner, K. E. (2001) · Applied Optics · doi:10.1364/ao.40.000656 · Cited by 8 · see citing works
Composite video artifact removal by nonlinear bilateral filtering
Kim, S.-H., & Hong, K. (2005) · Visual Communications and Image Processing · doi:10.1117/12.631409 · Cited by 6 · see citing works
Enhancement of image edge sharpness and acutance
Das, A., & Rangayyan, R. M. (1997) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.271116 · Cited by 5 · see citing works
Multi-Scale Image Sharpening with Noise Reduction
Kotera, H., & Wang, H. (2002) · Technical programs and proceedings/Technical program and proceedings · doi:10.2352/issn.2169-4451.2002.18.1.art00038_2 · Cited by 4 · see citing works
Improved Resolution Synthesis for Image Interpolation
Zhang, B., Gondek, J. S., Schramm, M. T., & Allebach, J. P. (2006) · Technical programs and proceedings/Technical program and proceedings · doi:10.2352/issn.2169-4451.2006.22.1.art00007_2 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
1 of the 54 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Kang, X., Li, S., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2264508 · Cited by 704 · see citing works
Weighted Guided Image Filtering
Li, Z. G., Zheng, J., Zhu, Z., Yao, W., & Wu, S. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2371234 · Cited by 568 · see citing works
Deep learning for lung Cancer detection and classification
Asuntha, A., & Srinivasan, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08394-3 · Cited by 383 · see citing works
RAISR: Rapid and Accurate Image Super Resolution
Romano, Y., Isidoro, J. R., & Milanfar, P. (2016) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2629284 · Cited by 287 · see citing works
A robust deformed convolutional neural network (CNN) for image denoising
Zhang, Q., Xiao, J., Tian, C., Lin, J. C., & Zhang, S. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12110 · Cited by 251 · see citing works
Image denoising based on non-local means filter and its method noise thresholding
Kumar, B. K. S. (2012) · Signal Image and Video Processing · doi:10.1007/s11760-012-0389-y · Cited by 179 · see citing works
Fast Adaptive Bilateral Filtering
Gavaskar, R. G., & Chaudhury, K. N. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2871597 · Cited by 170 · see citing works
Image denoising based on gaussian/bilateral filter and its method noise thresholding
Kumar, B. K. S. (2012) · Signal Image and Video Processing · doi:10.1007/s11760-012-0372-7 · Cited by 148 · see citing works
Enhancing underwater image via adaptive color and contrast enhancement, and denoising
Li, X., Hou, G., Li, K., & Pan, Z. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104759 · Cited by 148 · see citing works
Medical Image Denoising Using Bilateral Filter
Bhonsle, D., Chandra, V. K., & Sinha, G. R. (2012) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2012.06.06 · Cited by 117 · see citing works
Selectively Detail-Enhanced Fusion of Differently Exposed Images With Moving Objects
Li, Z. G., Zheng, J., Zhu, Z., & Wu, S. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2349432 · Cited by 105 · see citing works
The Guided Bilateral Filter: When the Joint/Cross Bilateral Filter Becomes Robust
Caraffa, L., Tarel, J., & Charbonnier, P. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2389617 · Cited by 100 · see citing works
Embedding Bilateral Filter in Least Squares for Efficient Edge-Preserving Image Smoothing
Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2890202 · Cited by 98 · see citing works
Deformation measurement in the presence of discontinuities with digital image correlation: A review
Hassan, G. M. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2020.106394 · Cited by 98 · see citing works
Weighted Guided Image Filtering With Steering Kernel
Sun, Z., Han, B., Li, J., Zhang, J., & Gao, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2928631 · Cited by 97 · see citing works
Underwater image enhancement by dehazing and color correction
Li, C., & Guo, J. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.3.033023 · Cited by 95 · see citing works
Effective Guided Image Filtering for Contrast Enhancement
Lu, Z., Long, B., Kang Li, B., & Fajin, L. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2867896 · Cited by 89 · see citing works
Review of Wafer Surface Defect Detection Methods
Jian-hong, M., Zhang, T., Yang, C., Cao, Y., Xie, L., Tian, H., & Li, X. (2023) · Electronics · doi:10.3390/electronics12081787 · Cited by 87 · see citing works
Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management
Chamola, V., Goyal, A., Sharma, P., Hassija, V., Bình, H. T. T., & Saxena, V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07087-7 · Cited by 81 · see citing works
An FPGA-Based Fully Synchronized Design of a Bilateral Filter for Real-Time Image Denoising
Gabiger-Rose, A., Kube, M., Weigel, R., & Rose, R. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2284133 · Cited by 78 · see citing works
Cinar, A. F., Barhli, S. M., Hollis, D., Flansbjer, M., Tomlinson, R. A., Marrow, T. J., & Mostafavi, M. (2017) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2017.04.010 · Cited by 78 · see citing works
Enhancement of Image and Depth Map Using Adaptive Joint Trilateral Filter
Jung, S. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2203734 · Cited by 70 · see citing works
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
Adaptive Fuzzy Filtering for Artifact Reduction in Compressed Images and Videos
Võ, D. T., Nguyen, T. Q., Yea, S., & Vetro, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2017341 · Cited by 69 · see citing works
Small Target Detection Using Bilateral Filter Based on Edge Component
Bae, T.-W., & Sohng, K. (2010) · Journal of Infrared Millimeter and Terahertz Waves · doi:10.1007/s10762-010-9633-0 · Cited by 65 · see citing works
Fast Bilateral Filtering for Denoising Large 3D Images
Pápari, G., Idowu, N., & Varslot, T. K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2624148 · Cited by 63 · see citing works
Anantrasirichai, N., Nicholson, L. B., Morgan, J. E., Erchova, I. A., Mortlock, K. E., North, R. V., Albon, J., & Achim, A. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.06.012 · Cited by 62 · see citing works
Fast Scale-Adaptive Bilateral Texture Smoothing
Ghosh, S., Gavaskar, R. G., Panda, D., & Chaudhury, K. N. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2916589 · Cited by 54 · see citing works
Fast and reliable image-noise estimation using a hybrid approach
Yang, S. M., & Tai, S.-C. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3476329 · Cited by 53 · see citing works
Adaptive Guided Image Filtering for Sharpness Enhancement and Noise Reduction
Pham, C. C., Ha, S. V. U., & Jeon, J. W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25367-6_29 · Cited by 52 · see citing works
Infrared and visible image fusion with spectral graph wavelet transform
Yan, X., Qin, H., Li, J., Zhou, H., & Zong, J. (2015) · Journal of the Optical Society of America A · doi:10.1364/josaa.32.001643 · Cited by 52 · see citing works
Spatial and temporal bilateral filter for infrared small target enhancement
Bae, T.-W. (2013) · Infrared Physics & Technology · doi:10.1016/j.infrared.2013.12.007 · Cited by 51 · see citing works
Analysis of quantum noise-reducing filters on chest X-ray images: A review
Chandra, T. B., & Verma, K. (2019) · Measurement · doi:10.1016/j.measurement.2019.107426 · Cited by 50 · see citing works
Fast Multilayer Laplacian Enhancement
Talebi, H., & Milanfar, P. (2016) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2607142 · Cited by 49 · see citing works
MIEGAN: Mobile Image Enhancement via a Multi-Module Cascade Neural Network
Pan, Z., Yuan, F., Lei, J., Li, W., Ling, N., & Kwong, S. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3054509 · Cited by 47 · see citing works
Efficient image sharpening and denoising using adaptive guided image filtering
Pham, C. C., & Jeon, J. W. (2014) · IET Image Processing · doi:10.1049/iet-ipr.2013.0563 · Cited by 45 · see citing works
A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning
Ajam, A., Aziz, A. B. A., Asirvadam, V. S., Muda, A. S., Faye, I., & Gardezi, S. J. S. (2017) · IEEE Access · doi:10.1109/access.2017.2718590 · Cited by 44 · see citing works
A new image denoising framework using bilateral filtering based non-subsampled shearlet transform
Routray, S., Malla, P. P., Sharma, S. K., Panda, S. K., & Palai, G. (2020) · Optik · doi:10.1016/j.ijleo.2020.164903 · Cited by 43 · see citing works
Fast single image haze removal via local atmospheric light veil estimation
Sun, W., Wang, H., Sun, C., Guo, B., Jia, W., & Sun, M. (2015) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2015.02.009 · Cited by 41 · see citing works
Robust Image Denoising Using a Virtual Flash Image for Monte Carlo Ray Tracing
Moon, B., Jun, J. Y., Lee, J., Kim, K., Hachisuka, T., & Yoon, S. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12004 · Cited by 41 · see citing works
Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images
Zhang, Y., Shi, F., Cheng, J., Wang, L., Yap, P., & Shen, D. G. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2786161 · Cited by 41 · see citing works
Improved bilateral filter for suppressing mixed noise in color images
Jin, L., Xiong, C., & Liu, H. (2012) · Digital Signal Processing · doi:10.1016/j.dsp.2012.06.012 · Cited by 41 · see citing works
Elaiyaraja, G., Kumaratharan, N., & Rao, T. C. S. (2019) · IETE Journal of Research · doi:10.1080/03772063.2019.1579679 · Cited by 40 · see citing works
Adaptive noise-reducing anisotropic diffusion filter
Abdallah, M. B., Malek, J., Azar, A. T., Belmabrouk, H., Monreal, J. E., & Krissian, K. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1933-9 · Cited by 40 · see citing works
Smoothing of ultrasound images using a new selective average filter
de Araújo, A. F., Constantinou, C. E., & Tavares, J. M. R. S. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.034 · Cited by 39 · see citing works
Image Enhancement by Entropy Maximization and Quantization Resolution Upconversion
Niu, Y., Wu, X., & Shi, G. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598485 · Cited by 39 · see citing works
Blurriness-Guided Unsharp Masking
Ye, W., & Ma, K. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2838660 · Cited by 39 · see citing works
Image-based porosity classification in Al-alloys by laser metal deposition using random forests
García‐Moreno, A., Alvarado-Orozco, J. M., Ibarra-Medina, J., & Martínez‐Franco, E. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05887-6 · Cited by 39 · see citing works
Multifocusing and Depth Estimation Using a Color Shift Model-Based Computational Camera
Kim, S., Lee, E., Hayes, M. H., III, & Paik, J. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2202671 · Cited by 38 · see citing works
Infrared and visible image fusion using multiscale directional nonlocal means filter
Yan, X., Qin, H., Li, J., Zhou, H., Zong, J., & Zeng, Q. (2015) · Applied Optics · doi:10.1364/ao.54.004299 · Cited by 38 · see citing works
Multispectral Image Denoising With Optimized Vector Bilateral Filter
Peng, H., Rao, R., & Dianat, S. A. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2287612 · Cited by 37 · see citing works
Entropy-based bilateral filtering with a new range kernel
Dai, T., Lu, W., Wang, W., Wang, J., & Xia, S. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.02.005 · Cited by 37 · see citing works
Correction of Axial and Lateral Chromatic Aberration With False Color Filtering
Chang, J., Kang, H., & Kang, M. G. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2228489 · Cited by 37 · see citing works
PReLU and edge‐aware filter‐based image denoiser using convolutional neural network
Thakur, R. S., Yadav, R. N., & Gupta, L. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0717 · Cited by 34 · see citing works
Adaptive sharpening of depth maps for 3D-TV
Silva, D., Fernando, W. A. C., Kodikaraarachchi, H., Worrall, S., & Kondoz, A. M. (2010) · Electronics Letters · doi:10.1049/el.2010.2320 · Cited by 34 · see citing works
Singh, A., Sethi, G., & Kalra, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.3003874 · Cited by 33 · see citing works
Bilateral kernel parameter optimization by risk minimization
Peng, H., & Rao, R. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5651045 · Cited by 32 · see citing works
Srinivasu, P. N., Bălaş, V. E., & Norwawi, N. M. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-5495-7_1 · Cited by 31 · see citing works
Saliency Guided Image Detail Enhancement
Ghosh, S., Gavaskar, R. G., & Chaudhury, K. N. (2019) · National Conference on Communications · doi:10.1109/ncc.2019.8732250 · Cited by 31 · see citing works
An effective and objective criterion for evaluating the performance of denoising filters
Zhang, Y., Cheng, H.-D., Huang, J., & Tang, X. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.015 · Cited by 31 · see citing works
Sahadevan, A. S., Routray, A., Das, B. S., & Ahmad, S. (2016) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.10.025004 · Cited by 30 · see citing works
García‐Moreno, A., Alvarado-Orozco, J. M., Ibarra-Medina, J., & Martínez‐Franco, E. (2021) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2020.12.048 · Cited by 30 · see citing works
Shehin, A. U., & Sankar, D. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104075 · Cited by 29 · see citing works
Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network
Sun, M., Lv, S., Zhao, X., Li, R., Zhang, W., & Zhang, X. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73564-1_13 · Cited by 29 · see citing works
Lane Detection and Tracking For Intelligent Vehicles: A Survey
FENICHE, M., & Mazri, T. (2019) · International Conference of Computer Science and Renewable Energies (ICCSRE) · doi:10.1109/iccsre.2019.8807727 · Cited by 29 · see citing works
Robust multi-scale weighting-based edge-smoothing filter for single image dehazing
Yadav, S. K., & Sarawadekar, K. P. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110137 · Cited by 29 · see citing works
DEEPORCD: Detection of Oral Cancer using Deep Learning
Dharani, R., & Revathy, S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1911/1/012006 · Cited by 28 · see citing works
FPGA Implementation of Real-Time Edge-Preserving Filter for Video Noise Reduction
Vinh, T. Q., Park, J. H., Kim, Y., & Hong, S. H. (2008) · International Conference on Computer and Electrical Engineering · doi:10.1109/iccee.2008.61 · Cited by 28 · see citing works
Routray, S., Ray, A. K., & Mishra, C. (2018) · Optik · doi:10.1016/j.ijleo.2018.01.096 · Cited by 28 · see citing works
Scale‐adaptive Structure‐preserving Texture Filtering
Song, C., Xiao, C., Lei, L., & Sui, H. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13824 · Cited by 28 · see citing works
Talebi, H., & Milanfar, P. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2348870 · Cited by 27 · see citing works
Volumetric Estimation of the Damaged Area in the Human Brain from 2D MR Image
Srinivasu, P. N., Rao, T. S., & Bălaş, V. E. (2020) · International Journal of Information System Modeling and Design · doi:10.4018/ijismd.2020010105 · Cited by 27 · see citing works
Ramya, J., Maheswari, B. U., Rajakumar, M. P., & Sonia, R. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.4.28052 · Cited by 29 · see citing works
Unsharp Structure Guided Filtering for Self-Supervised Low-Dose CT Imaging
Wu, Q., Ji, X., Gu, Y., Xiang, J., Quan, G., Li, B., Zhu, J., Coatrieux, G., Coatrieux, J.-L., & Chen, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3280217 · Cited by 27 · see citing works
Automatic illumination and color compensation using mean shift and sigma filter
Han, H. C., & Sohn, K. (2009) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2009.5278052 · Cited by 27 · see citing works
Accelerating spatially varying Gaussian filters
Baek, J., & Jacobs, D. E. (2010) · ACM Transactions on Graphics · doi:10.1145/1882261.1866191 · Cited by 27 · see citing works
Constrained Unsharp Masking for Image Enhancement
Bilcu, R. C., & Markku Vehviläinen, B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69905-7_2 · Cited by 27 · see citing works
A fast color image enhancement algorithm based on Max Intensity Channel
Sun, W., Long, H., Guo, B., Jia, W., & Sun, M. (2014) · Journal of Modern Optics · doi:10.1080/09500340.2014.897387 · Cited by 26 · see citing works
Depth map artefacts reduction: a review
Ibrahim, M. M., Liu, Q., Khan, R., Yang, J., Adeli, E., & Yang, Y. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1622 · Cited by 26 · see citing works
Surface design of wood-based board to imitate wood texture using 3D printing technology
Feng, X., Wu, Z., Sang, R., Wang, F., Zhu, Y., & Wu, M. (2019) · BioResources · doi:10.15376/biores.14.4.8196-8211 · Cited by 25 · see citing works
Oral Cancer Detection Using Deep Learning Approach
Yadav, R. K., Ujjainkar, P. A., & Moriwal, R. (2023) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs57921.2023.10062993 · Cited by 24 · see citing works
Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis
Lu, Y., Chan, H., Wei, J., Hadjiiski, L. M., & Samala, R. K. (2014) · Medical Physics · doi:10.1118/1.4903283 · Cited by 24 · see citing works
Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs
Spagnolo, F., Corsonello, P., Frustaci, F., & Perri, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06084-y · Cited by 23 · see citing works
Automatic license plate recognition using mobile device
Do, H. N., Vo, M.-T., Vuong, B. Q., Pham, H. T., Nguyen, A. H., & Luong, H. Q. (2016) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc.2016.7764786 · Cited by 23 · see citing works
Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement
Sengupta, D., Biswas, A., & Gupta, P. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09583-1 · Cited by 22 · see citing works
Restoration for weakly blurred and strongly noisy images
Zhu, X., & Milanfar, P. (2011) · Proceedings · doi:10.1109/wacv.2011.5711490 · Cited by 22 · see citing works
Double Bilateral Filtering for Image Noise Removal
Chang, H.-H., & Chu, W. C. (2009) · doi:10.1109/csie.2009.414 · Cited by 22 · see citing works
Comparative evaluation of methods for filtering Kinect depth data
Essmaeel, K., Gallo, L., Damiani, E., Pietro, G. D., & Dipandà, A. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-1982-6 · Cited by 21 · see citing works
Diwakar, M., Singh, P., Swarup, C., Bajal, E., Jindal, M., Ravi, V., Singh, K. U., & Singh, T. (2022) · Diagnostics · doi:10.3390/diagnostics12112766 · Cited by 21 · see citing works
Locally adaptive bilateral clustering for image deblurring and sharpness enhancement
Toh, K. V., & Isa, N. A. M. (2011) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2011.6018878 · Cited by 21 · see citing works
Smoothing vs. sharpening of colour images: Together or separated
Pérez, C. P. S., Morillas, S., Jordán, C., & Conejero, J. A. (2017) · Applied Mathematics and Nonlinear Sciences · doi:10.21042/amns.2017.1.00025 · Cited by 21 · see citing works
Improved Denoising Auto-Encoders for Image Denoising
Xiang, Q., & Pang, X. (2018) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2018.8633143 · Cited by 21 · see citing works
Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering
Taassori, M. (2024) · Sensors · doi:10.3390/s24216849 · Cited by 20 · see citing works
Accelerating spatially varying Gaussian filters
Baek, J., & Jacobs, D. E. (2010) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1866158.1866191 · Cited by 20 · see citing works
Content adaptive guided image filtering
Li, Z. G., Zheng, J., & Zhu, Z. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890136 · Cited by 20 · see citing works
Review on applications of computer vision techniques for pipeline inspection
Liu, Y., Moghaddas, S. A., Shi, S., Huang, Y., Kong, J., & Bao, Y. (2025) · Measurement · doi:10.1016/j.measurement.2025.117370 · Cited by 19 · see citing works
A synchronous FPGA design of a bilateral filter for image processing
Gabiger-Rose, A., Kube, M., & Weigel, R. (2009) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2009.5414894 · Cited by 19 · see citing works
Dash, S., Senapati, M. R., & Jena, U. R. (2018) · Egyptian Informatics Journal · doi:10.1016/j.eij.2018.01.003 · Cited by 19 · see citing works
Image Enhancement With PDEs and Nonconservative Advection Flow Fields
Jaouen, V., Bert, J., Boussion, N., Fayad, H. J., Hatt, M., & Visvikis, D. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2881838 · Cited by 19 · see citing works
Entropy-based trilateral filtering for noise removal in digital images
Chang, H.-H. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647219 · Cited by 19 · see citing works
- Authors
- Zhang, B., & Allebach, J. P.
- Affiliations
- Texas Instruments (United States) (US)
- Purdue University West Lafayette (US)
- ORCID
- Allebach, J.P.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2008-05
- Volume
- 17
- Issue
- 5
- Pages
- 664-678
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2145367339
- Cited by
- 403
- References
- 54
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 16 · 2023: 36 · 2022: 20 · 2021: 25 · 2020: 19 · 2019: 27 · 2018: 31 · 2017: 26 · 2016: 24 · 2015: 29 · 2014: 31 · 2013: 24 · 2012: 27
- FWCI
- 19.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- adaptive bilateral filter, image sharpening, noise reduction, histogram transformation, unsharp masking, halo artifacts, edge enhancement, texture enhancement, text images
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Graphics, Image Enhancement, Image Interpretation, Computer-Assisted, Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Reproducibility of Results, Information Storage and Retrieval, Artifacts
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review