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Works37 from public data
- Neural Degradation Representation Learning for All-in-One Image Restoration98
This paper proposes to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations that adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components.
- NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results52
The first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4, develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development.
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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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- EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration34
Experimental results demonstrate that the proposed Enhanced Deep Pyramid Network (EDPN) significantly outperforms existing solutions for blurry image super-resolution and blurry image deblocking.
- Mutual-Guided Dynamic Network for Image Fusion33
A novel mutual-guided dynamic network (MGDN) is proposed for image fusion, which allows for effective information utilization across different locations and inputs and outperforms existing methods on four image fusion tasks.
- Generalized Lightness Adaptation with Channel Selective Normalization28
A novel generalized lightness adaptation algorithm that extends conventional normalization techniques through a channel filtering design, dubbed Channel Selective Normalization (CSNorm), which purposely normalizes the statistics of lightness-relevant channels and keeps other channels unchanged, so as to improve feature generalization and discrimination.
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- Stereo Video Super-Resolution via Exploiting View-Temporal Correlations24
A novel Stereo Video Super-Resolution Network (SVSRNet) is proposed to fulfill the StereoVSR task via exploiting view-temporal correlations to enforce consistency constraint of superresolved stereo videos.
- Optimization Algorithm of Steel Surface Defect Detection Based on YOLOv8n-SDEC16
The study improves the original SPPF module to the SPPCSPC module, enabling the network to better emphasize the features of the target, and introduces a fusion with deformable convolution, enhancing the extraction of features from defective targets.
- Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation16
This paper introduces a novel continuous spectral representation, NeSR, and embraces the concept of implicit function and implements a parameterized embodiment with a neural network to extend the flexibility of spectral reconstruction by enabling an arbitrary number of spectral bands as the target output.
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- Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer11
A two-stage target detection method that combines Cascade RCNN and Swin Transformer models that can accurately identify and localize farmland pests, which can help improve farmland’s ecological environment is proposed.
- Continuous Spatial-Spectral Reconstruction via Implicit Neural Representation9
The proposed neural spatial-spectral representation (NeSSR), which projects the deep features extracted from low-resolution inputs to the corresponding intensity values under target 3D coordinates, allows for spectral image reconstruction with arbitrary resolutions in both spatial and spectral dimensions for the first time.
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- Event-Boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction7
A Dynamic-Static Decomposition strategy that first identifies dynamic areas by exploiting the inability of static Gaussians to represent motions, then applies a buffer-based soft decomposition to separate dynamic and static areas, which accelerates rendering by avoiding unnecessary deformation in static areas, and focuses on dynamic areas to enhance fidelity.
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- EdiTor: Edge-guided Transformer for Ghost-free High Dynamic Range Imaging7
A novel Edge-guided Transformer framework (EdiTor) customized for ghost-free HDR reconstruction, where the long-range motions across different exposures can be delicately modeled by incorporating the edge prior, which outperforms state-of-the-art methods both quantitatively and qualitatively.
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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