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- Structure-Preserving Deraining with Residue Channel Prior Guidance180
A Structure-Preserving Deraining Network (SPDNet) with RCP guidance is proposed, which directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions.
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- Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation70
A Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the stereo image super-resolution and disparity estimation in a unified framework and interact them with each other to further improve their performance.
- Efficient and Accurate Multi-Scale Topological Network for Single Image Dehazing58
A multi-scale topological network that provides a large number of search paths that enable the network to extract abundant image features as well as strong fault tolerance and robustness and adaptively select important features and ignore interference information when fusing different scale representations is proposed.
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- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
- Fine-Structure Preserved Real-World Image Super-Resolution Via Transfer Vae Training27
A Transfer VAE Training (TVT) strategy to transfer the $8 \times$ downsampled VAE into a $4 \times$ one while adapting to the pre-trained UNet, which significantly improves fine-structure preservation, while requiring fewer FLOPs than state-of-the-art onestep diffusion models.
- NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge24
This challenge established a new benchmark for real-world image restoration, featuring diverse scenarios with and without reference ground truth, and tasked participants with restoring real-captured images suffering from complex and unknown degradations.
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- NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge22
The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications, which improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges.
- NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results21
An overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images demonstrates the growing progress in this challenging task.
- High-Frequency Modulated Transformer for Multi-Contrast MRI Super-Resolution21
A lightweight and accurate High-frequency Modulated Transformer for multi-contrast MRI super-resolution by employing an enhancement module to enhance and amplify the high-frequency priors in the reference and target modalities and a novel cross-attention mechanism to fuse the high-frequency enhanced features with the global features sequentially.
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- Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation15
The degradation mechanism of the noisy image is explored and a lightweight Multiple Degradation and Reconstruction Network (MDRN) is proposed to progressively remove noise to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions.
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- LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results8
A detailed analysis of the submitted methods and corresponding results is provided, emphasizing recent progress in unified real-world image restoration and establishing a benchmark for future research in real-world low-level vision.
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- Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction5
An effective progressive back-traced dehazing network based on multi-resolution recurrent reconstruction strategies that could restore satisfactory high-frequency textures and high-fidelity colors is proposed.
- GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution4
Group Direct Preference Optimization (GDPO) is proposed, a novel approach to integrate RL into one-step generative ISR model training and an attribute-aware reward function is designed to dynamically evaluate the score of each sample based on its statistics of smooth and texture areas.
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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 2026-10-16. 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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