LLDiffusion: Learning degradation representations in diffusion models for low-light image enhancement

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117

Proceedings European Conference on Computer Vision 2002

Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153

Restormer: Efficient Transformer for High-Resolution Image Restoration

Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00564 · Cited by 4,737

LIME: Low-Light Image Enhancement via Illumination Map Estimation

Guo, X., Li, Y., & Ling, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639450 · Cited by 2,998

The Retinex Theory of Color Vision

Land, E. H. (1977) · Scientific American · doi:10.1038/scientificamerican1277-108 · Cited by 2,981

EnlightenGAN: Deep Light Enhancement Without Paired Supervision

Jiang, Y., Gong, X., Liu, D., Cheng, Y., Chen, F., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,852

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00185 · Cited by 2,632

Uformer: A General U-Shaped Transformer for Image Restoration

Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., & Li, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01716 · Cited by 2,597

Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey

Li, X., Ren, Y., Jin, X., Lan, C., Wang, X., Zeng, W., Wang, X., & Chen, Z. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02570-9 · Cited by 219

On the design fundamentals of diffusion models: A survey

Chang, Z., Koulieris, G. A., Chang, H. J., & Shum, H. P. H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111934 · Cited by 158

Diffusion Models in Low-Level Vision: A Survey

He, C., Shen, Y., Fang, C., Xiao, F., Tang, L., Zhang, Y., Zuo, W., Guo, Z., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545047 · Cited by 145

Low-Light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion

Xue, M., He, J., Wang, W., & Zhou, M. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3764933 · Cited by 63

Arbitrary-Scale Image Generation and Upsampling Using Latent Diffusion Model and Implicit Neural Decoder

Kim, J., & Kim, T.-K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00879 · Cited by 38

Efficient Diffusion as Low Light Enhancer

Lan, G., Ma, Q., Yang, Y., Wang, Z., Wang, D., Li, X., & Zhao, B. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01982 · Cited by 25

Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark

Lin, X., Yue, J., Ding, S., Ren, C., Qi, L., & Yang, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3487849 · Cited by 19

Detail-Preserving Diffusion Models for Low-Light Image Enhancement

Huang, Y., Liao, X., Liang, J., Shi, B., Xu, Y., & Callet, P. L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3502801 · Cited by 22

Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models

Huang, Y. L., Liao, X., Liang, J., Quan, Y., Shi, B., & Xu, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i4.32398 · Cited by 14

Student Classroom Behavior Recognition Based on YOLOv8 and Attention Mechanism

Zhang, J., Guo, L., & Wang, X. (2025) · Information · doi:10.3390/info16110934 · Cited by 13

Adaptive Luminance Enhancement and High-Fidelity Color Correction for Low-Light Image Enhancement

Niu, Y., Li, F., Li, Y., Chen, S., & Chen, Y. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3564112 · Cited by 10

LersGAN: A GAN-Based Model for Low-Light Remote Sensing Image Enhancement

Li, T., Guo, T., & Xiang, D. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3608696 · Cited by 10

Low-Light Image Enhancement via Diffusion Models With Semantic Priors of Any Region

Zeng, X., Zhu, L., Yang, W., Leung, H. W. H., Wang, S., & Kwong, S. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3617320 · Cited by 10

UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation

Zhao, S., Lu, W., Wang, B., Wang, T., Zhang, K., & Zhao, H. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3707458 · Cited by 10

A Comprehensive Review of Image Restoration Research Based on Diffusion Models

Li, J., Wang, H., Li, Y., & Zhang, H. (2025) · Mathematics · doi:10.3390/math13132079 · Cited by 11

Semi-LLIE: Semi-supervised contrastive learning with Mamba-based low-light enhancement

Li, G., Zhang, K., Wang, T., Li, M., Zhao, B. C., & Li, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108010 · Cited by 10

HPCDNet: Hybrid position coding and dual-frquency domain transform network for low-light image enhancement

Chen, M., Li, H., Peng, H., Xiong, X., & Long, N. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024085 · Cited by 8

A Retinex‐Based Network for Low‐Light Image Enhancement With Multi‐Scale Denoising and Focal‐Aware Reflections

Ji, P., Lv, Z., & Zhang, Z. (2025) · IET Image Processing · doi:10.1049/ipr2.70059 · Cited by 7

UPI 2 Diff: Restoring Underwater Polarization Image and Information in Turbid Conditions via Polarization Guided Diffusion Model

Liu, H., Qiu, M., Cao, H., Ma, Q., Li, X., & Hu, H. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3656897 · Cited by 7

LIIS: Low-light image instance segmentation

Li, W., Huang, Y., Zhang, X., & Han, G. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104116 · Cited by 7

JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement

Wu, Y., Wang, G., Wang, Z., Yang, Y., Li, T., Zhang, M., Li, C., & Shen, H. T. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680876 · Cited by 13

A Simple Yet Robust Nonlinear Function for Low-Light Image Enhancement Task

Razafindratovolahy, A., & Rao, Y. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3602001 · Cited by 5

DLFE-Net: Preserving Details and Removing Noise Using HVI Color Space for Low-Light Image Enhancement

He, Z., Yuan, X., Hao, G., & Wang, W. (2025) · Sensors · doi:10.3390/s25175353 · Cited by 4

Controllable diffusion generated dataset and hybrid CNN–Mamba network for burned area segmentation

Fu, Y., Fang, W., & Fu, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104057 · Cited by 6

Striving for Faster and Better: A One-Layer Architecture With Auto Re-Parameterization for Low-Light Image Enhancement

An, N., Ma, L., Han, G., Fan, X., & Liu, R. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3547029 · Cited by 4

XGFu: Enhancing low-light visualization by feature and graph fusion of multiple artificial exposure images

Qiao, S., Wang, T., An, M., Chen, R., & Li, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128308 · Cited by 4

AdaptDiff: Adaptive diffusion learning for low-light image enhancement

Shao, X., Zhang, G., Shen, Y., Zhang, B., Wang, Z., & Sun, Y. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104439 · Cited by 4

Gradient-Reversed Domain-Generalizable Multi-Modal Cross-Attention Network for Robust Face Anti-Spoofing

Yadav, K. D. K., Kavati, I., & Cheruku, R. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3616651 · Cited by 6

DCGANet: Fusing selective variable convolution and dynamic content-guided attention for infrared small target detection

Chen, Y., Zhu, Y., Min, S., Qiu, Z., Hu, A., Wang, T., & Zhang, T. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115546 · Cited by 4

SIANet: Self-calibrating iterative attention network for low-light image enhancement

Zhang, W., Gao, X., Xu, J., Zhuang, P., Liang, Z., & Zhao, W. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.115048 · Cited by 4

Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement

Adhikarla, E., Zhang, K., VidalMata, R. G., Aithal, M. B., Madhusudhana, N. A., Nicholson, J., Sun, L., & Davison, B. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78110-0_17 · Cited by 4

An Efficient Illumination Estimation Algorithm for Low-Light Image Enhancement

Wu, T., Glavin, M., & Jones, E. (2025) · IEEE Access · doi:10.1109/access.2025.3578916 · Cited by 4

Dynamic alignment and diffusion models for multi-modal 3D object detection in bird’s-eye view

Li, T., wang, H., Tang, X., & Cao, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae02b2 · Cited by 3

RetinexD-Net: A retinex-densenet fusion architecture for low-light image enhancement in drill pipe detection

Zhang, Z., Liu, X., Hua, D., Guo, X., Wang, T., & Liu, X. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113547 · Cited by 3

An unsupervised low-light image enhancement method for underground scenes using low- and high-frequency enhancement calibration

Zhuli, R., Zhu, H., Yuan, R., ZHANG, J., & Zhao, L. (2025) · Measurement · doi:10.1016/j.measurement.2025.120176 · Cited by 3

FSILLIE: Frequency spatial integrative-aware guidance for low-light image enhancement

Yu, Y., Wang, G., Qin, H., Ma, L., Peng, X., & Zhang, X. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132630 · Cited by 3

Stokes vector-based mamba for low-light color polarimetric image enhancement

Gao, M., Zhao, G., He, X., Jin, X., Duan, J., & Huilin, J. (2026) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109595 · Cited by 3

Towards fast and effective low-light image enhancement via adaptive Gamma correction and detail refinement

Xu, S., Chen, Q., Peng, L., Hu, H., & Tao, W. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104724 · Cited by 3

Nighttime apple harvesting image enhancement based on a global–local feature fusion diffusion model

Xu, L., Ni, T., Zhou, F., Lin, Q., Chen, L., Luo, Q., Xia, K., Xue, X., Zheng, X., Wu, S., et al. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111672 · Cited by 3

All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network

Wang, B., Qin, B., Li, J., Xu, F., Sun, F., & Xiong, H. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3675478 · Cited by 3

SAOPQ: Spatial-attention cross-information fusion with feature-guided orthogonal product quantization learning for scalable face image retrieval

Hannane, R., Elboushaki, A., & Afdel, K. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115930 · Cited by 3

Dual-attention cooperative and multi-view gated transformer for adverse weather removal

Liang, S., Wen, Y., Gao, T., Li, Z., Qin, J., & Chen, T. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113633 · Cited by 3

Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

Zhao, H., Xu, S., Peng, L., Hu, H., & Jiang, S. (2025) · Applied Sciences · doi:10.3390/app15137382 · Cited by 3

Retinex With Progressive Decomposition and Multi-Scale Denoising for Low Light Image Enhancement

Qiao, S., Chen, R., An, M., Wang, D., & Li, Y. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3603184 · Cited by 3

Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image

Xie, S., Ma, Y., Xu, W., Qiu, S., & Sun, Y. (2023) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei60920.2023.10373367 · Cited by 4

Progressive Prompt-Driven Low-Light Image Enhancement With Frequency Aware Learning

Sun, X., Cheng, D., Li, Y., Wang, N., Zhang, D., Gao, X., & Sun, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3586101 · Cited by 2

Gradient-Guided Diffusion-Based Restoration of Extremely Compressed Backgrounds for Video Coding for Machines

Dao, L. T. H., Vien, A. G., Lee, J., Jeong, S., Yang, N., & Lee, C. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3598710 · Cited by 2

Frequency-conditional diffusion model for low-light image enhancement

Zhao, L., Wang, J., Fan, C., Zheng, Z., & Zhang, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131383 · Cited by 2

Low-light image enhancement via self-degradation-aware and semantic-perceptual guidance networks

Ahmad, O. S., Anjuman, S. A., Bakr, S. S., & Bartani, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114571 · Cited by 2

ReciprocalLA-LLIE: Low-light image enhancement with luminance-aware reciprocal diffusion process

Lin, Z., Chen, W. P., Xu, J., Zeng, D., & Chen, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131438 · Cited by 2

Rethinking progressive low-light image enhancement: A frequency-aware tripartite multi-scale network

Li, Y., Zhang, K., Zhou, X., Zhang, Z., & Hu, S. Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108351 · Cited by 2

Vision model fine-tuning based on two-level prompts fusion

Mao, K., & Fang, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115185 · Cited by 2

Maximum-Value retinex decomposition guided generative priors for joint deraining and low-light image enhancement

Sun, Y., Wang, J., & Yin, R. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123136 · Cited by 2

TOVO: Tone-oriented vision optimization for efficient low-light image enhancement

Razafindratovolahy, A., Rao, Y., Tovolahy, J. C., Afanga, M., & Mutale, A. (2026) · Signal Processing · doi:10.1016/j.sigpro.2026.110540 · Cited by 2

A Visual-Linguistic Approach for Robust RGB-Thermal Tracking With Dynamic Template Adaptation

Wang, X., Zheng, H., Liao, H., Zhang, Q., Ma, L., & Jiang, J. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3668565 · Cited by 2

Semi-supervised teaching graph contrastive network for node classification

Shen, C., Song, J., Sun, Q., Guo, Q., & Tsang, E. C. C. (2026) · Electronic Research Archive · doi:10.3934/era.2026074 · Cited by 2

Social graph diffusion and disentangled representations learning for multimodal recommendation

Zhang, Z., Zhang, H., Wang, B., & Zhu, J. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00612-x · Cited by 2

When One-Shot Federated Learning Meets Diffusion Models at the Edge: Technological Advances and Applications

Chen, W., Deng, D., Xiang, C., Liu, Z., & Xiao, B. (2026) · Journal of Intelligent Computing and Networking · doi:10.64509/jicn.21.64 · Cited by 2

An efficient deep unfolding approach for snapshot compressed imaging of motion videos

Yin, J., Tao, C., Hu, B., Wang, Y., & Wang, Q. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113554 · Cited by 2

SSFA-Net: Sparse strip and dual-domain spatial-frequency attention for efficient image dehazing

Babar, A. H., Hossain, M. S., Hussain, M., Zou, L., Gu, N., & Huang, Z. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133671 · Cited by 2

A wavelet-fusion attention prior network for low-light image enhancement

Li, Y., Wang, R., Yan, H., Luo, H., & Yin, J. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106132 · Cited by 2

VCR: Variance-Driven Channel Recalibration for Robust Low-Light Enhancement

Cheng, Z., Zhang, F., Yin, X., Yin, B., & Wang, H. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3689584 · Cited by 2

A spatially guided dual-path network for hyperspectral and multispectral image fusion

Liu, R., Li, J., Wang, P., & Zhu, C. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116227 · Cited by 2

WaveCD: Physics-guided wavelet cold diffusion for low-light image denoising

Chen, Z., CHEN, Y., Qin, P., Zhao, P., Chai, R., & Zeng, J. (2026) · Signal Processing · doi:10.1016/j.sigpro.2026.110730 · Cited by 2

Multi-scale cascaded network with high-low frequency for low-light image enhancement

Wu, J., Ran, T., Xiao, W., Yuan, L., & Tao, Q. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104380 · Cited by 2

ColorAssist: Perception-Based Recoloring for Color Vision Deficiency Compensation

Lin, L., Xie, S., Wang, Y., Chen, B., Xue, Y., Zhuang, X., & Zhao, T. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3602643 · Cited by 2

Illuminating the Shadows: Enhanced Low-Light Image via a Retinex-based Model with Color Equalization

Liu, Z. M., Fan, T., Zhang, W., Wang, W., Huang, J., Lan, R., & Lu, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129039 · Cited by 2

Incorporating Fourier Transformation With Diffusion Models for Low-Light Image Enhancement

Ma, A., Jiang, H., Liang, B., & Han, S. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3575570 · Cited by 1

Deep visualization prior based video dehazing: a hybrid approach for enhanced visibility

Khaliq, Y., Irtaza, A., & Shah, S. M. A. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04289-z · Cited by 1

MVDT: Multiview Distillation Transformer for View‐Invariant Sign Language Translation

Guan, Z., Hu, Y., Jiang, H., Sun, Y., & Yin, B. (2025) · IET Computer Vision · doi:10.1049/cvi2.70038 · Cited by 1

Dual-stream noise suppression low-light image enhancement network based on Retinex theory

Han, G., Zeng, B., Rui, Z., Hu, Y., & Yu, X. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04514-9 · Cited by 1

Adversarial multimodal user-generated contents generation for anti-user identity linkage

Gao, J., Wu, B., Zheng, K., & Wu, C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114978 · Cited by 1

Noise-Resilient Low-Light Image Enhancement with CLIP Guidance and Pixel-Reordering Subsampling

Song, S. (2025) · Electronics · doi:10.3390/electronics14244839 · Cited by 1

DFD-Stereo: Dual-Domain Feature Decoupling for Stereo Matching

Zhu, Z., Sun, F., Sun, J., & Li, H. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3633461 · Cited by 1

CoDiFSR: Code Diffusion paradigm with prior knowledge distillation for face super-resolution

Gu, J., Su, N., Wang, Z., Chen, S., Yang, W., & Wang, G. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113745 · Cited by 1

Complementary encoder refinement and multilevel crossmodal interaction for multimodal sarcasm detection

Guo, H., Huang, S., Geng, Z., Chen, J., Liu, S., & Kong, C. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116541 · Cited by 1

LL-Refiner: Learning Adaptive Refinement for Ultra-High-Definition Low-Light Image Enhancement

Yi, X., Yan, Q., Zhang, Y., Li, Z., Zhang, H., Cai, Z., & Ma, J. (2026) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2026.125939 · Cited by 1

MARINE-Net: Physics-guided lightweight CNN-INR architecture for underwater image restoration and color enhancement

Sriram, S., Vijayaraj, N., Swamynathan, V. P., Choudhary, G., & Thangavel, M. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114463 · Cited by 1

Physics-guided atmospheric restoration network for single image dehazing

Hou, J., Gendy, G., & He, G. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04411-1 · Cited by 1

Codebook prior-guided hybrid attention dehazing network

Huang, L., Zheng, H., Pan, L., Su, Z., & Wu, Q. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105700 · Cited by 1

S 3 learning: image enhancement using optimized very deep homomorphic filtering enabled image enhancer

Prakash, V. N. V. S., Basha, M. M., Faruk, S. K. U., Madhurima, V., & Gundala, S. (2025) · The Imaging Science Journal · doi:10.1080/13682199.2025.2550060 · Cited by 1

Defense against unauthorized distillation in image restoration via feature space perturbation

Hu, H., Zheng, Z., & Lyu, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131984 · Cited by 1

USER: User-Side modality representation enhancement for multimodal recommendation

Fan, Y., Shi, D., Aguilar, J., & Zurada, J. M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114943 · Cited by 1

InfoBFR: Real-World Blind Face Restoration via Information Bottleneck

Gao, N., Li, J., Huang, H., Zeng, Z., & He, R. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3637231 · Cited by 1

SWSTformer: A stationary wavelet-spatial attention multi-scale transformer framework for single image deraining

Xu, F., Xiong, R., Diao, L., Yang, H., Tong, N., & Li, F. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-05010-w · Cited by 1

Prompt-guided selective frequency network for real-world scene text image super-Resolution

Yan, X., Shan, T., Qin, H., Akhtar, N., Liu, Y., Rahmani, H., & Mian, A. S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2025.112974 · Cited by 1

MoLA: Molecular multimodal layerwise adaptive network for molecular property prediction

Li, J., Zhang, Z. H., Lei, Z., Cheng, J., Ma, L., Liu, C., & Gao, S. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115563 · Cited by 1

LFR-CMT-3D: Lipschitz-regularized multi-modal fusion for robust object detection in open-pit mines

Zhang, H., Liu, Y., Fang, Y., Liu, P., Yang, J., & Zhang, X. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae58c7 · Cited by 1

GA-CLIP: Dual-branch CLIP for generalizable and anomaly-aware zero-shot anomaly detection

Guo, W., Wan, Y., Yuan, S., Chang, L., Wu, Y., & Dou, Z. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116078 · Cited by 1

BIG-TM: Bridging Individual Guidance with Trifusion MoPoE for Chinese memes understanding

Wang, T., Li, J., Xue, F., Su, D., Su, G., Wen, X., & Sha, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116137 · Cited by 1

PriP: A training-free low-light image enhancement framework via content and illumination synergistic guidance

Liu, Y., Dong, X., Jin, Z., & Sha, W. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104676 · Cited by 1

P-PatchDiff: Progressive Patch Diffusion Models for Low-Light Image Enhancement

Guo, R., Zhong, H., Pagnucco, M., & Song, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02995-w · Cited by 1

KANDiff: Low-light Image Enhancement Based on Diffusion Models

Ren, Y., Yue, M., He, Y., & Zhang, L. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554251342571 · Cited by 2

Part-Attention-Based Pseudo-Label Refinement Reciprocal Compact Loss for Unsupervised Cattle Face Recognition

Liu, P., & Zhao, J. (2025) · Electronics · doi:10.3390/electronics14122343 · Cited by 1

MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing

Sung, J., Roh, C., Eom, C., & Oh, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2025.132477

Perceiving degradation prompts in residual diffusion model for real image denoising

Liu, M., Long, X., Tang, Q., Yao, C., & Zhao, Y. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114090

A blur-guided multi-attention network based on left-right consistency for gradual defocus deblurring in binocular images

Chen, Z., Tang, Z., Zhong, Y., Zhang, H., Dai, Z., & Xie, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131853

DMCDiff: Dynamic manifold constraint guided diffusion models for semi-supervised low-light image enhancement

Wu, X., Yu, Y., Ben, X., & Chen, L. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105663

NOFA++: Tuning-Free NeRF-Based One-Shot Facial Avatar Reconstruction

Yu, W., Feng, C., Yuan, L., & Tian, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3628119

CDMM: conditional diffusion model with mamba for low-light underwater image enhancement

Zheng, J., Lu, J., Zhao, R., Fu, Y., Liu, J., Luo, Z., & Li, X. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07981-6

Denoising diffusion post-processing for low-light image enhancement

Panagiotou, S., & Bosman, A. S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110799 · Cited by 66

Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement

Xu, L., Hu, C., Zhu, W., Wu, F., Cai, Z., Ye, M., & Lu, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109622 · Cited by 13

LLDE: Enhancing Low-Light Images with Diffusion Model

Ooi, X. P., & Chan, C. S. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222446 · Cited by 22

Degrade Is Upgrade: Learning Degradation for Low-Light Image Enhancement

Jiang, K., Wang, Z., Wang, Z., Chen, C., Yi, P., Lü, T., & Lin, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19992 · Cited by 65

Robust low-light image enhancement in the wild via data synthesis and generative diffusion prior

Wang, Z., Lin, Q., Liu, F., Zhang, W., & Zhou, W. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113336 · Cited by 5