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Works196 from public data
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- Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal938
This work introduces a deep network architecture called DerainNet for removing rain streaks from an image based on the deep convolutional neural network (CNN), which directly learns the mapping relationship between rainy and clean image detail layers from data.
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- A fusion-based enhancing method for weakly illuminated images739
A fusion-based method for enhancing various weakly illuminated images that requires only one input to obtain the enhanced image and represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image.
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- Lightweight Pyramid Networks for Image Deraining421
This paper finds that by introducing the mature Gaussian–Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters.
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- Underwater image enhancement with global–local networks and compressed-histogram equalization228
This work proposes a two-branch network to compensate the global distorted color and local reduced contrast, respectively, and designs a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training.
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- Rain Streak Removal via Dual Graph Convolutional Network168
This work proposes a simple yet effective dual graph convolutional network (GCN) for single image rain removal that is easy to implement, end-to-end trainable and computationally efficient and achieves significant improvements over the recent state-of-the-art methods.
- Spatial-Frequency Domain Information Integration for Pan-Sharpening158
This paper proposes a Spatial-Frequency Information Integration Network, dubbed as SFIIN, which employs the standard convolution to integrate the local information of two modalities of PAN and MS images in the spatial domain and adopts deep Fourier transformation to achieve the image-wide receptive field for exploring global contextual information.
- Single-Image-Based Rain and Snow Removal Using Multi-guided Filter154
This paper proposes a new rain and snow removal method through using low frequency part of a single image based on a key difference between clear background edges and rain streaks or snowflakes, which has good performance in rain removal andSnow removal.
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- Residual-Guide Network for Single Image Deraining148
A novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts high-quality reconstruction while using fewer parameters than previous methods is proposed.
- RETRACTED: Pan-Sharpening with Customized Transformer and Invertible Neural Network145
This is the first attempt to introduce transformer and invertible neural network into pan-sharpening field and outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops.
- Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI143
A Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled \(k \) -space data is developed, and the proposed regularization framework can improve reconstruction accuracy over other methods.
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- Illumination-Invariant Person Re-Identification100
This work proposes a illumination-invariant person re-identification framework which is able to simultaneously achieve Retinex illumination decomposition and personRe-id, and designs a bottom-up attention network to remove the effect of weak illumination and obtain the enhanced image without introducing over-enhancement.
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- Efficient Model-Driven Network for Shadow Removal84
This work proposes a new shadow illumination model, which ensures the identity mapping among unshaded regions, and adaptively performs fine grained spatial mapping between shadow regions and their references, and reformulates the shadow removal task as a variational optimization problem.
- Real-World Person Re-Identification via Degradation Invariance Learning84
By introducing a self-supervised disentangled representation learning strategy, this framework is able to simultaneously extract identity-related robust features and remove real-world degradations without extra supervision and is ableTo achieve state-of-the-art performance on several Re-ID benchmarks.
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- Revisiting Single Image Reflection Removal in the Wild62
An advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs is devised, and a reflection location-aware cascaded framework specifically tailored for SIRR is designed.
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- Successive Graph Convolutional Network for Image De-raining55
This paper proposes a graph convolutional networks (GCNs)-based model and introduces a simple yet effective recurrent operations to perform the de-raining process in a successive manner and achieves state-of-the-art results on both synthetic and real-world data sets.
- Space-Time Video Super-Resolution Using Temporal Profiles54
A novel space-time video super-resolution method, which aims to recover a high-frame-rate and high-resolution video from its low- frame- rate and low-resolution observation, followed by a refining module for artifacts alleviation and detail enhancement, which does not require any explicit or implicit motion estimation.
- Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement53
This paper proposes a novel rank learning guided no-reference quality assessment method for UIE, trained based on an elaborately formulated self-supervision mechanism to train a Siamese Network to learn their quality rankings.
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- Single Image Shadow Detection via Complementary Mechanism50
A novel shadow detection framework by investigating the mutual complementary mechanisms contained in this specific task, which achieves superior shadow detection performance against state-of-the-art methods with a relatively low computational cost.
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- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
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- NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results28
This paper presents an overview of NTIRE 2025, the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results, and anticipate that this challenge will drive further advancements in event-based vision research.
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- Ntire 2025 Image Shadow Removal Challenge Report22
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge, with 17 teams successfully submitting their solutions during the final evaluation phase, and one focusing on reconstruction fidelity and the other on visual perception through a user study.
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- The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview20
A review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes and offering an in-depth overview of the latest trends in the field.
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- Man-Made Object Recognition from Underwater Optical Images Using Deep Learning and Transfer Learning14
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- MIPI 2024 Challenge on Demosaic for Hybridevs Camera: Methods and Results9
A total of 110 participants from both industrial and academic backgrounds contributed many valuable solutions to address the difficulty of the restoration of HybridEVS’s raw data, thus raising the reconstructed performance to a new height.
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- AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results3
This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
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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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