A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works

Segment Anything

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works

Image Super-Resolution Using Deep Convolutional Networks

Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · see citing works

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570 · see citing works

Making a “Completely Blind” Image Quality Analyzer

Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766 · see citing works

No-Reference Image Quality Assessment in the Spatial Domain

Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Agustsson, E., & Timofte, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.150 · Cited by 4,295 · see citing works

Image segmentation using deep learning: A survey

Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796 · see citing works

Accelerating the Super-Resolution Convolutional Neural Network

Dong, C., Loy, C. C., & Tang, X. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_25 · Cited by 3,786 · see citing works

Scene Parsing through ADE20K Dataset

Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.544 · Cited by 3,326 · see citing works

Structure-Measure: A New Way to Evaluate Foreground Maps

Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092 · see citing works

Semantic Understanding of Scenes Through the ADE20K Dataset

Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1140-0 · Cited by 1,831 · see citing works

Sparse Representation for Color Image Restoration

Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716 · see citing works

From learning models of natural image patches to whole image restoration

Zoran, D., & Weiss, Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126278 · Cited by 1,522 · see citing works

Image denoising: Can plain neural networks compete with BM3D?

Burger, H. C., Schuler, C. J., & Harmeling, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247952 · Cited by 1,325 · see citing works

LVIS: A Dataset for Large Vocabulary Instance Segmentation

Gupta, A., Dollar, P., & Girshick, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00550 · Cited by 1,274 · see citing works

Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model

Li, M., Liu, J., Yang, W., Sun, X., & Guo, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2810539 · Cited by 1,245 · see citing works

Attention-guided CNN for image denoising

Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.024 · Cited by 688 · see citing works

Rep ViT: Revisiting Mobile CNN From ViT Perspective

Wang, A., Chen, H., Lin, Z., Han, J., & Ding, G. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01506 · Cited by 752 · see citing works

Automatic photo pop-up

Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616 · see citing works

Shrinkage Fields for Effective Image Restoration

Schmidt, U., & Roth, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.349 · Cited by 543 · see citing works

Ecological statistics of Gestalt laws for the perceptual organization of contours

Elder, J. H., & Goldberg, R. M. (2002) · Journal of Vision · doi:10.1167/2.4.5 · Cited by 333 · see citing works

EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction

Cai, H., Li, J., Hu, M., Gan, C., & Han, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01587 · Cited by 356 · see citing works

Robust guided image filtering using Nonconvex potentials

Ham, B., Cho, M., & Ponce, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2669034 · Cited by 219 · see citing works

Supervised Learning of Image Restoration with Convolutional Networks

Jain, V., Murray, J. F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K. L., Helmstaedter, M. N., Denk, W., & Seung, H. S. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408909 · Cited by 217 · see citing works

Recovering Occlusion Boundaries from a Single Image

Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408985 · Cited by 206 · see citing works

Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos

Liu, J., Yang, W., Yang, S., & Guo, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00341 · Cited by 193 · see citing works

Accurate Blur Models vs. Image Priors in Single Image Super-resolution

Efrat, N., Glasner, D., Apartsin, A., Nadler, B., & Levin, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.352 · Cited by 200 · see citing works

Learning to generate images with perceptual similarity metrics

Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8297089 · Cited by 186 · see citing works

Transformation of General Binary MRF Minimization to the First-Order Case

Ishikawa, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.91 · Cited by 144 · see citing works

Direct face detection and video reconstruction from event cameras

Miyatani, Y., Barua, S., & Veeraraghavan, A. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477561 · Cited by 133 · see citing works

Diffusion Models, Image Super-Resolution, and Everything: A Survey

Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117 · see citing works

Training an Active Random Field for Real-Time Image Denoising

Barbu, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028254 · Cited by 105 · see citing works

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

Wang, Y., Wang, L., Wang, H., & Li, P. (2019) · IEEE Access · doi:10.1109/access.2019.2903582 · Cited by 143 · see citing works

Bayesian natural selection and the evolution of perceptual systems

Geisler, W. S., & Diehl, R. L. (2002) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2001.1055 · Cited by 101 · see citing works

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

Pal, A., Garain, U., Chandra, A., Chatterjee, R., & Senapati, S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.027 · Cited by 74 · see citing works

Semantic Curiosity for Active Visual Learning

Chaplot, D. S., Jiang, H., Gupta, S., & Gupta, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_19 · Cited by 64 · see citing works

Image super-resolution with densely connected convolutional networks

Kuang, P., Ma, T., Chen, Z., & Li, F. (2019) · Applied Intelligence · doi:10.1007/s10489-018-1234-y · Cited by 33 · see citing works

CAAT: Image super-resolution algorithm via channel attention and transformer

Chen, Y., Chen, L., Xia, R., Yang, K., & Zou, K. (2025) · Array · doi:10.1016/j.array.2025.100628 · Cited by 29 · see citing works

Median Filter Aided CNN Based Image Denoising: An Ensemble Approach

Dey, S., Bhattacharya, R., Schwenker, F., & Sarkar, R. (2021) · Algorithms · doi:10.3390/a14040109 · Cited by 22 · see citing works

ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing

Santos, C. F. G. D., Arrais, R. R., Silva, J. V. S. D., Silva, M. H. M. D., Neto, W. B. G. D. A., Lopes, L. T., Bileki, G. A., Lima, I. O., Rondon, L. B., Souza, B. M. D., et al. (2025) · ACM Computing Surveys · doi:10.1145/3708516 · Cited by 9 · see citing works