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Works115 from public data
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks4,943
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
- RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution412
This work first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality, and shows that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics.
- FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing332
A fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing which takes frequency information as additional priors and can generator more natural and realistic dehazed images with less color distortion and fewer artifacts.
- Learn to Match: Automatic Matching Network Design for Visual Tracking256
This work introduces six novel matching operators from the perspective of feature fusion instead of explicit similarity learning, namely Concatenation, Pointwise-Addition, Pairwise-Relation, FiLM, Simple-Transformer and Transductive-Guidance, to explore more feasibility on matching operator selection.
- Blind Image Super-Resolution: A Survey and Beyond228
A taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used to solve the SR model is proposed, which helps summarize and distinguish among existing methods.
- A Survey of Reinforcement Learning for Large Reasoning Models175
Research applying RL to LLMs and LRMs for reasoning abilities is examined, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area.
- Conditional Sequential Modulation for Efficient Global Image Retouching156
An extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching is proposed that achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively.
- Masked Image Training for Generalizable Deep Image Denoising143
This work presents a novel approach to enhance the generalization performance of denoising networks, known as masked training, which involves masking random pixels of the input image and reconstructing the missing information during training.
- HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization126
This work proposes a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization, which achieves the state-of-the-art performance in quantitative comparisons and visual quality.
- Enhanced Quadratic Video Interpolation100
This work improves the performance of QVI from three facets and proposes an enhanced quadratic video interpolation (EQVI) model that won the first place in the AIM2020 Video Temporal Super-Resolution Challenge and devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process.
- Temporally consistent video colorization with deep feature propagation and self-regularization learning67
A novel temporally consistent video colorization (TCVC) framework that effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of colorization and introduces a self-regularization learning (SRL) scheme to minimize the differences in predictions obtained using different time steps.
- RankSRGAN: Super Resolution Generative Adversarial Networks With Learning to Rank64
This work first train a Ranker which can learn the behaviour of perceptual metrics and then introduces a novel rank-content loss to optimize the perceptual quality, and extends the method to multiple Rankers to provide multi-dimension constraints for the generator.
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- SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM47
This work introduces Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer, an open-source extension to 3D Slicer extensively used by the medical imaging community.
- Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects47
The generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the back-ground and the Unknown Object Detection Benchmark is presented, the first publicly benchmark that encompasses precision evaluation for unknown detection to the authors' knowledge.
- Unifying Image Processing as Visual Prompting Question Answering44
Inspired by NLP question answering techniques, PromptGIP treats the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem, eliminating the need for task-specific finetuning.
- A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds44
This work proposes a unified gated degradation model to generate a broad set of degradation cases using a random gate controller and proposes simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases.
- FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution42
Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves state-of-the-art performance with up to 12x speedup over prior one-step diffusion VSR models.
- ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding40
ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities, and ArtiMuse-10K, the first expert-curated image aesthetic dataset, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score are presented.
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- MagHacker39
MagHacker is a new sensing system that realizes such eavesdropping attack over commodity mobile devices, which monitor and analyze the magnetic field being produced by the stylus pen's internal magnet.
- UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results38
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
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- Evaluating the Generalization Ability of Super-Resolution Networks34
This work makes the first attempt to propose a Generalization Assessment Index for Super-Resolution (SR) networks, namely SRGA, which exploits the statistical characteristics of the internal features of deep networks to measure the generalization ability.
- Very Lightweight Photo Retouching Network With Conditional Sequential Modulation34
An extremely lightweight framework – Conditional Sequential Retouching Network (CSRNet) that achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively and can be easily extended to learn local enhancement effects.
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 2026-10-14. 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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