SRDiff: Single image super-resolution with diffusion probabilistic models

Image quality assessment: from error visibility to structural similarity

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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

Enhanced Deep Residual Networks for Single Image Super-Resolution

Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117 · 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

Deep Learning Face Attributes in the Wild

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747 · see citing works

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178 · see citing works

Improved Techniques for Training GANs

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · see citing works

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_18 · Cited by 6,003 · see citing works

Deeply-Recursive Convolutional Network for Image Super-Resolution

Kim, J., Lee, J. K., & Lee, K. M. (2016) · 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/cvpr.2016.181 · Cited by 3,122 · see citing works

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Sajjadi, M. S. M., Schölkopf, B., & Hirsch, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.481 · Cited by 1,088 · see citing works

Multi-scale Residual Network for Image Super-Resolution

Li, J., Fang, F., Mei, K., & Zhang, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_32 · Cited by 951 · see citing works

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01410 · Cited by 665 · see citing works

Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T. S., & Shi, H. (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/cvpr42600.2020.00573 · Cited by 442 · see citing works

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

Mao, Q., Lee, H.-Y., Tseng, H.-Y., Ma, S., & Yang, M. (2019) · 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/cvpr.2019.00152 · Cited by 442 · see citing works

RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution

Zhang, W., Liu, Y., Dong, C., & Qiao, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00319 · Cited by 418 · see citing works

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

Lugmayr, A., Danelljan, M., Gool, L. V., & Timofte, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_42 · Cited by 390 · see citing works

Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

Guo, Y. F., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., & Tan, M. (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/cvpr42600.2020.00545 · Cited by 380 · see citing works

ODE-Inspired Network Design for Single Image Super-Resolution

He, X., Mo, Z., Wang, P., Liu, Y., Yang, M., & Cheng, J. (2019) · 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/cvpr.2019.00183 · Cited by 270 · see citing works

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

Rad, M. S., Bozorgtabar, B., Marti, U.-V., Basler, M., Ekenel, H. K., & Thiran, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00280 · Cited by 171 · see citing works

Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution

Qiu, Y., Wang, R., Tao, D., & Cheng, J. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00428 · Cited by 145 · see citing works

Super Resolution for Remote Sensing Images Based on a Universal Hidden Markov Tree Model

Li, F., Jia, X., Fraser, D. J., & Lambert, A. J. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2031636 · Cited by 97 · see citing works

Image super-resolution using a dilated convolutional neural network

Lin, G., Wu, Q., Qiu, L., & Huang, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.062 · Cited by 96 · see citing works

De Novo -Designed Near-Infrared Nanoaggregates for Super-Resolution Monitoring of Lysosomes in Cells, in Whole Organoids, and in Vivo

Fang, H., Yao, S., Chen, Q., Liu, C., Cai, Y., Geng, S., Bai, Y., Tian, Z., Zacharias, A. L., Takebe, T., et al. (2019) · ACS Nano · doi:10.1021/acsnano.9b08011 · Cited by 73 · see citing works

Evaluation of image resolution and super-resolution on face recognition performance

Fookes, C., Lin, F. C., Chandran, V., & Sridharan, S. (2011) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2011.06.004 · Cited by 72 · see citing works

Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Losses

Cheon, M., Kim, J.-H., Choi, J., & Lee, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_4 · Cited by 49 · see citing works

Single image super-resolution with multi-level feature fusion recursive network

Jin, X., Xiong, Q., Xiong, C., Li, Z., & Gao, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.102 · Cited by 34 · see citing works

A two-channel convolutional neural network for image super-resolution

Li, S., Fan, R., Lei, G., Yue, G., & Hou, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.041 · Cited by 32 · see citing works

Adaptive Density Estimation for Generative ModelsPreprint

Lucas, T., Shmelkov, K., Alahari, K., Schmid, C., & Verbeek, J. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.01091 · Cited by 24 · see citing works

RBPNET: An asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image

Chen, X., Wang, X., Lu, Y., Li, W., Wang, Z., & Huang, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.079 · Cited by 21 · see citing works

Image super-resolution based on residually dense distilled attention network

Dun, Y., Da, Z., Yang, S., & Qian, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.008 · Cited by 11 · see citing works

Generative collaborative networks for single image super-resolution

Seddik, M. E. A., Tamaazousti, M., & Lin, J. Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.068 · Cited by 8 · see citing works

Self-Controllable Super-Resolution Deep Learning Framework for Surveillance Drones in Security Applications

Park, S., Kang, Y., Park, J., & Kim, J. (2020) · ICST Transactions on Security and Safety · doi:10.4108/eai.30-6-2020.165502 · Cited by 7 · see citing works

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704 · see citing works

Diffusion models in medical imaging: A comprehensive survey

Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102846 · Cited by 850 · see citing works

Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., & Kreis, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02161 · Cited by 724 · see citing works

A Survey on Generative Diffusion Models

Cao, H., Tan, C., Gao, Z., Xu, Y., Chen, G., Heng, P., & Li, S. Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3361474 · Cited by 679 · see citing works

DiffIR: Efficient Diffusion Model for Image Restoration

Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412 · see citing works

Diffusion Autoencoders: Toward a Meaningful and Decodable Representation

Preechakul, K., Chatthee, N., Wizadwongsa, S., & Suwajanakorn, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01036 · Cited by 338 · see citing works

Image super-resolution: A comprehensive review, recent trends, challenges and applications

Lepcha, D. C., Goyal, B., Dogra, A., & Goyal, V. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.007 · Cited by 323 · see citing works

Implicit Diffusion Models for Continuous Super-Resolution

Gao, S., Liu, X., Zeng, B., Xu, S. H., Li, Y., Luo, X., Liu, J., Zhen, X., & Zhang, B. (2023) · 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/cvpr52729.2023.00966 · Cited by 299 · see citing works

EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

Xiao, Y., Yuan, Q., Jiang, K., He, J., Jin, X., & Zhang, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3341437 · Cited by 300 · see citing works

Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction

Chung, H., Sim, B., & Ye, J. C. (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/cvpr52688.2022.01209 · Cited by 285 · see citing works

Deblurring via Stochastic Refinement

Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., & Milanfar, P. (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/cvpr52688.2022.01581 · Cited by 286 · see citing works

A physics-informed diffusion model for high-fidelity flow field reconstruction

Shu, D., Li, Z., & Farimani, A. B. (2023) · Journal of Computational Physics · doi:10.1016/j.jcp.2023.111972 · Cited by 218 · see citing works

Automated data processing and feature engineering for deep learning and big data applications: A survey

Mumuni, A., & Mumuni, F. (2024) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2024.01.002 · Cited by 192 · see citing works

Guided Motion Diffusion for Controllable Human Motion Synthesis

Karunratanakul, K., Preechakul, K., Suwajanakorn, S., & Tang, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00205 · Cited by 172 · see citing works

Notice of Removal: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., & Tan, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00984 · Cited by 144 · see citing works

Q-Diffusion: Quantizing Diffusion Models

Li, X., Liu, Y., Lian, L., Yang, H., Dong, Z., Kang, D. D., Zhang, S., & Keutzer, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01608 · Cited by 156 · see citing works

Diffusion models in bioinformatics and computational biology

Guo, Z., Liu, J., Wang, Y., Chen, M., Wang, D., Xu, D., & Cheng, J. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00114-9 · Cited by 163 · see citing works

WaveDM: Wavelet-Based Diffusion Models for Image Restoration

Huang, Y., Huang, J., Liu, J., Yan, M., Yu, D., Lv, J., Chen, C., & Chen, S. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3359769 · Cited by 142 · see citing works

ResDiff: Combining CNN and Diffusion Model for Image Super-resolution

Shang, S., Shan, Z., Liu, G., Wang, L., Wang, X., Zhang, Z., & Zhang, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28746 · Cited by 151 · see citing works

Diffusion Model-Based Image Editing: A Survey

Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 168 · see citing works

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 170 · 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

ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis

Tian, Y., Li, C., Huang, J., Xiao, X., Zhang, X., Li, Y., & Fu, B. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110343 · Cited by 120 · see citing works

Denoising Pretraining for Semantic Segmentation

Brempong, E. A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., & Norouzi, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00462 · Cited by 120 · see citing works

CoLa-Diff: Conditional Latent Diffusion Model for Multi-modal MRI Synthesis

Jiang, L., Mao, Y., Wang, X., Chen, X., & Li, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_38 · Cited by 108 · see citing works

Developing an AI-based automated fashion design system: reflecting the work process of fashion designers

Choi, W., 장, 세., Kim, H. Y., Lee, Y., Lee, S.-G., Lee, H., & Park, S. (2023) · Fashion and Textiles · doi:10.1186/s40691-023-00360-w · Cited by 105 · see citing works

A review of deep-learning-based super-resolution: From methods to applications

Su, H., Li, Y., Xu, Y., Fu, X., & Liu, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110935 · Cited by 107 · see citing works

GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature-Guided DDPM

Wen, Y., Ma, X., Zhang, X., & Pun, M.-O. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3381752 · Cited by 103 · see citing works

SAR Despeckling Using a Denoising Diffusion Probabilistic Model

Perera, M. V., Nair, N. G., Bandara, W. G. C., & Patel, V. M. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3270799 · Cited by 100 · see citing works

Diffusion model with disentangled modulations for sharpening multispectral and hyperspectral images

Cao, Z., Cao, S., Deng, L.-J., Wu, X., Hou, J., & Vivone, G. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102158 · Cited by 99 · see citing works

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 109 · see citing works

Efficient Diffusion Model for Image Restoration by Residual Shifting

Yue, Z., Wang, J., & Loy, C. C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3461721 · Cited by 100 · see citing works

Super-resolution of brain MRI images based on denoising diffusion probabilistic model

Wu, Z., Chen, X., Xie, S., Shen, J. G., & Zeng, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104901 · Cited by 92 · see citing works

TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models

Cao, T., Kreis, K., Fidler, S., Sharp, N., & Yin, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00385 · Cited by 96 · see citing works

Multiscale Structure Guided Diffusion for Image Deblurring

Ren, M., Delbracio, M., Talebi, H., Gerig, G., & Milanfar, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00984 · Cited by 99 · see citing works

Comprehensive exploration of diffusion models in image generation: a survey

Chen, H., Xiang, Q., Hu, J., Ye, M., Yu, C., Cheng, H., & Zhang, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11110-3 · Cited by 104 · see citing works

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation

Jiang, H., Imran, M. A., Zhang, T., Zhou, Y., Liang, M., Gong, K., & Shao, W. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3565183 · Cited by 100 · see citing works

LDMVFI: Video Frame Interpolation with Latent Diffusion Models

Danier, D., Zhang, F., & Bull, D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27912 · Cited by 89 · see citing works

DifFace: Blind Face Restoration With Diffused Error Contraction

Yue, Z., & Loy, C. C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3432651 · Cited by 99 · see citing works

Opportunities and challenges of diffusion models for generative AI

Chen, M., Song, M., Fan, J., & Wang, M. (2024) · National Science Review · doi:10.1093/nsr/nwae348 · Cited by 141 · see citing works

A novel lightweight DDPM-based data augmentation method for rotating machinery fault diagnosis with small sample

Fan, C., Zhang, Y., Ma, H., Yu, K., & Ma, Z. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112741 · Cited by 80 · see citing works

A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution

Li, J., Pei, Z., Li, W., Gao, G., Wang, L., Wang, Y., & Zeng, T. (2024) · ACM Computing Surveys · doi:10.1145/3659100 · Cited by 83 · see citing works

Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

Wang, J., Du, H., Niyato, D. T., Xiong, Z., Kang, J., Ai, B., Han, Z., & Kim, D. I. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3414628 · Cited by 77 · see citing works

Consistent View Synthesis with Pose-Guided Diffusion Models

Tseng, H.-Y., Li, Q., Kim, C., Alsisan, S., Huang, J., & Kopf, J. (2023) · 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/cvpr52729.2023.01609 · Cited by 76 · see citing works

CRS-Diff: Controllable Remote Sensing Image Generation With Diffusion Model

Tang, D., Cao, X., Hou, X., Jiang, Z., Liu, J., & Meng, D. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3453414 · Cited by 81 · see citing works

Lightweight image super-resolution for IoT devices using deep residual feature distillation network

Mardieva, S., Ahmad, S., Umirzakova, S., Rasool, M. J. A., & Whangbo, T. K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111343 · Cited by 71 · see citing works

Underwater image enhancement method based on denoising diffusion probabilistic model

Lu, S., Guan, F., Zhang, H., & Lai, H. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103926 · Cited by 70 · see citing works

ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

Niu, A., Pham, T. X., Zhang, K., Sun, J., Zhu, Y., Yan, Q., Kweon, I. S., & Zhang, Y. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2024.3374122 · Cited by 72 · see citing works

A Recent Survey of Vision Transformers for Medical Image Segmentation

Khan, A., Rauf, Z., Khan, A. R., Rathore, S., Khan, S. H., Shah, N. S., Farooq, U., Asif, H., Asif, A., Zahoora, U., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3618215 · Cited by 92 · see citing works

Speed-Up DDPM for Real-Time Underwater Image Enhancement

Lu, S., Guan, F., Zhang, H., & Lai, H. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3314767 · Cited by 68 · see citing works

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry

Tortora, L. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1346059 · Cited by 72 · see citing works

RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model

Bie, F., Yang, Y., Zhou, Z., Ghanem, A., Zhang, M., Yao, Z., Wu, X., Holmes, C., Golnari, P. A., Clifton, D. A., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3522305 · Cited by 76 · see citing works

Urban wind field prediction based on sparse sensors and physics‐informed graph‐assisted auto‐encoder

Gao, H., Hu, G., Zhang, D., Jiang, W., Tse, K. T., Kwok, K. C. S., & Kareem, A. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13147 · Cited by 64 · see citing works

DocDiff: Document Enhancement via Residual Diffusion Models

Yang, Z., Liu, B., Xiong, Y., Yi, L., Wu, G., Tang, X., Liu, Z., Zhou, J., & Zhang, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611730 · Cited by 61 · see citing works

Single image super-resolution with denoising diffusion GANS

Xiao, H., Wang, X., Wang, J., Cai, J., Deng, J.-H., Yan, J.-K., & Tang, Y.-D. (2024) · Scientific Reports · doi:10.1038/s41598-024-52370-3 · Cited by 59 · see citing works

MDNet: Mamba-Effective Diffusion-Distillation Network for RGB-Thermal Urban Dense Prediction

Zhou, W., Wu, H., & Jiang, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3508058 · Cited by 61 · see citing works

Conditional Diffusion Models for Camouflaged and Salient Object Detection

Sun, K., Chen, Z., Lin, X., Sun, X., Liu, H., & Ji, R. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3527469 · Cited by 59 · see citing works

Diffusion Model for Camouflaged Object Detection

Chen, Z., Gao, R., Xiang, T.-Z., & Lin, F. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230302 · Cited by 60 · see citing works

A data-driven framework for designing microstructure of multifunctional composites with deep-learned diffusion-based generative models

Lee, K., Lim, H. J., & Yun, G. J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107590 · Cited by 60 · see citing works

Learning to Efficiently Sample from Diffusion Probabilistic ModelsPreprint

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DisC-Diff: Disentangled Conditional Diffusion Model for Multi-contrast MRI Super-Resolution

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Optimized single-image super-resolution reconstruction: A multimodal approach based on reversible guidance and cyclical knowledge distillation

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Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model

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A Diffusion Model Translator for Efficient Image-to-Image Translation

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Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

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CrowdDiff: Multi-Hypothesis Crowd Density Estimation Using Diffusion Models

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DiffuStereo: High Quality Human Reconstruction via Diffusion-Based Stereo Using Sparse Cameras

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

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DiffTAD: Denoising diffusion probabilistic models for vehicle trajectory anomaly detection

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3D-aware Image Generation using 2D Diffusion Models

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DiffAM: Diffusion-Based Adversarial Makeup Transfer for Facial Privacy Protection

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Generative Artificial Intelligence Meets Synthetic Aperture Radar: A survey

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AnlightenDiff: Anchoring Diffusion Probabilistic Model on Low Light Image Enhancement

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MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation

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A comb concatenation diffusion model for hyperspectral image super-resolution

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VidToMe: Video Token Merging for Zero-Shot Video Editing

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Artificial-Intelligence-Generated Content with Diffusion Models: A Literature Review

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Image Super-Resolution with Deep Variational Autoencoders

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CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising

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Generating Content for HDR Deghosting from Frequency View

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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Face Animation with an Attribute-Guided Diffusion Model

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The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection

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SinDiffusion: Learning a Diffusion Model From a Single Natural Image

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Controlled graph neural networks with denoising diffusion for anomaly detection

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High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

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Deep generative model super-resolves spatially correlated multiregional climate data

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Optimizing Diffusion Noise Can Serve As Universal Motion Priors

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Building Bridges Across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model

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Diffusion-based Blind Text Image Super-Resolution

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Multi-depth branch network for efficient image super-resolution

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CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution

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MRI Super-Resolution With Partial Diffusion Models

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RGBD2: Generative Scene Synthesis via Incremental View Inpainting Using RGBD Diffusion Models

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TransC-GD-CD: Transformer-Based Conditional Generative Diffusion Change Detection Model

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Multimodal Conditioned Diffusion Model for Recommendation

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Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

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EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising

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Diffusion model-assisted cross-domain fault diagnosis for rotating machinery under limited data

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A Conditional Diffusion Model With Fast Sampling Strategy for Remote Sensing Image Super-Resolution

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Single image super-resolution with denoising diffusion GANS

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Single image super-resolution with channel attention and diffusion

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Diffusion models for image super-resolution: State-of-the-art and future directions

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CCIR: high fidelity face super-resolution with controllable conditions in diffusion models

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ImpRes: implicit residual diffusion models for image super-resolution

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