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- Shuicheng YanSuggested from co-authorship
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Works30 from public data
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- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- NTIRE 2024 Challenge on Image Super-Resolution (×4): Methods and Results83
This paper reviews the NTIRE 2024 challenge on image super-resolution (×4), highlighting the solutions proposed and the outcomes obtained, and offers a comprehensive overview of current trends in this field.
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- FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring59
A lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than Sota, and 5x faster than SOTA is proposed.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data37
NoiSER without access to any task-related data, simply learns a convolutional neural network equipped with an instance-normalization layer by taking a random noise image and feeding it to the trained network for predicting the normal-light image.
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- CRNet: Unsupervised Color Retention Network for Blind Motion Deblurring27
A novel unsupervised color retention network termed CRNet is proposed to perform blind motion deblurring and a new module called pyramid global blur feature perception (PGBFP) is proposed, to quantitatively prove the effectiveness of the network in color retention.
- 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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- MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results18
The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal and a detailed description of all models developed is provided in this paper.
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- CropCap: Embedding Visual Cross-Partition Dependency for Image Captioning11
This paper proposes a novel image captioner embedding visual cross-partition dependency, dubbed CropCap, where the visual sequence generated from the Swin Transformer-based pre-embedding network is fed into the proposed cross- partition dependency module to refinedly model the interaction between partial representations on both the time step and embedded dimension.
- FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection7
A novel deepfake detection framework learning distributional disparity based on the differentiated reconstruction on real and fake images for improved generalization and avoids the influence from the specificalization of the known fake patterns.
- Unsupervised Color Retention Network and New Quantization Metric for Blind Motion Deblurring7
A general unsupervised color retention network termed CRNet for blind motion deblurring is proposed, which can be easily extended to other tasks suffering from chromatic aberration and new concepts of blur offset estimation and adaptive blur correction are introduced, so that more detailed information can be retained to improve thedeblurring task.
- Individual/joint deblurring and low-light image enhancement in one go via unsupervised deblurring paradigm6
A novel architecture with a transformation branch and a self-regression branch is elaborated, which only accepts unpaired blurry-sharp data as input to train the model; in this way, the pre-trained model can be surprisingly applied to both LLIE, deblurring and mixed degradation processing.
- When low-light meets flares: Towards Synchronous Flare Removal and Brightness Enhancement3
A novel Fourier transform-based deep FLLIE network termed Synchronous Flare Removal and Brightness Enhancement (SFRBE) is developed and a Residual Directional Fourier Block (RDFB) is introduced that learns in the frequency domain to extract accurate global information and capture detailed features from multiple directions.
Publication data from OpenAlex; 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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