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- Multi-Scale Progressive Fusion Network for Single Image Deraining859
This work explores the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi- scale progressive fusion network (MSPFN) for single image rain streak removal.
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- EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution281
Different from previous works using heavy UNet for noise prediction, an efficient activation network (EANet) is developed to achieve favorable noise prediction performance by simplified channel attention and simple gate operation, which dramatically reduces the computational budget.
- Hierarchical dense recursive network for image super-resolution242
A novel hierarchical dense connection network (HDN) is advocated for image SR that outperforms the state-of-the-art methods in terms of quantitative indicators and realistic visual effects, as well as enjoys a fast and accurate reconstruction.
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- Unpaired Deep Image Deraining Using Dual Contrastive Learning205
An effective unpaired SID adversarial framework which explores mutual properties of the unpaired exemplars by a dual contrastive learning manner in a deep feature space, named as DCD-GAN is developed.
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- Local-Global Temporal Difference Learning for Satellite Video Super-Resolution180
A Short-term Temporal Difference Module is devised to extract local motion representations from RGB difference maps between adjacent frames, which yields more clues for accurate texture representation and a Difference Compensation Unit (DCU) is proposed to enrich the interaction between the spatial distribution of the target frame and temporal compensated results.
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- Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution144
Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.
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- A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends107
This survey provides the first in-depth and systematic overview of AiOIR, delivering a structured taxonomy that categorizes existing methods by architectural designs, learning paradigms, and their core innovations, and systematically categorizes current approaches and assess the challenges these models encounter.
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- Magic ELF: Image Deraining Meets Association Learning and Transformer102
This paper aims to unify CNN and Transformer to take advantage of their learning merits for image deraining, and introduces degradation prior to help background recovery and presents the association refinement deraining scheme.
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- Ultra-dense GAN for satellite imagery super-resolution89
An ultra-dense GAN (udGAN) is proposed for image SR, where the internal layout of the residual block is reformed into a two-dimensional matrix topology, which can provide additional diagonal connections so that the model can still accomplish enough pathways with fewer layers.
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- Degrade Is Upgrade: Learning Degradation for Low-Light Image Enhancement68
A novel two-step generation network for degradation learning and content refinement, which is not only superior to one-step methods, but also capable of synthesizing sufficient paired samples to benefit the model training.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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