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abikaki committed Feb 12, 2024
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4 changes: 2 additions & 2 deletions README.md
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<a href="https://github.com/DmitryRyumin/CVPR-2023-Papers/blob/main/sections/2023/main/low-level-vision.md">Low-Level Vision</a>
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12 changes: 6 additions & 6 deletions sections/2023/main/low-level-vision.md
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| Optimization-Inspired Cross-Attention Transformer for Compressive Sensing | [![GitHub](https://img.shields.io/github/stars/songjiechong/OCTUF?style=flat)](https://github.com/songjiechong/OCTUF) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimization-Inspired_Cross-Attention_Transformer_for_Compressive_Sensing_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.13986-b31b1b.svg)](http://arxiv.org/abs/2304.13986) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=-WKuwpS0D9w) |
| Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution | [![GitHub](https://img.shields.io/github/stars/JNNNNYao/LINF?style=flat)](https://github.com/JNNNNYao/LINF) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Local_Implicit_Normalizing_Flow_for_Arbitrary-Scale_Image_Super-Resolution_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.05156-b31b1b.svg)](http://arxiv.org/abs/2303.05156) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=kB2sm_k8P6I) |
| Event-Based Frame Interpolation With Ad-Hoc Deblurring | [![GitHub](https://img.shields.io/github/stars/AHupuJR/REFID?style=flat)](https://github.com/AHupuJR/REFID) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2301.05191-b31b1b.svg)](http://arxiv.org/abs/2301.05191) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=pInRJ_O2kas) |
| Better ``CMOS`` Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution | | | |
| SMAE: Few-Shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders | | | |
| A Unified HDR Imaging Method with Pixel and Patch Level | | | |
| DegAE: A New Pretraining Paradigm for Low-Level Vision | | | |
| CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input | | | |
| Blind Video Deflickering by Neural Filtering with a Flawed Atlas | | | |
| Better ``CMOS`` Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution | [![GitHub](https://img.shields.io/github/stars/ByChelsea/CMOS?style=flat)](https://github.com/ByChelsea/CMOS) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.03542-b31b1b.svg)](http://arxiv.org/abs/2304.03542) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=Y12hM-lm3Ow) |
| SMAE: Few-Shot Learning for HDR Deghosting With Saturation-Aware Masked Autoencoders| :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.06914-b31b1b.svg)](http://arxiv.org/abs/2304.06914) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=iNBGNf8e3FE) |
| A Unified HDR Imaging Method With Pixel and Patch Level | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Yan_A_Unified_HDR_Imaging_Method_With_Pixel_and_Patch_Level_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.06943-b31b1b.svg)](http://arxiv.org/abs/2304.06943) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=f932i4j7ABI) |
| DegAE: A New Pretraining Paradigm for Low-Level Vision <br/> [![CVPR - Highlight](https://img.shields.io/badge/CVPR-Highlight-FFFF00)]() | [![GitHub](https://img.shields.io/github/stars/lyh-18/DegAE_DegradationAutoencoder?style=flat)](https://github.com/lyh-18/DegAE_DegradationAutoencoder) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=_u5oUOrSohY) |
| CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network With Large Input | [![GitHub](https://img.shields.io/github/stars/Sheldon04/CABM-pytorch?style=flat)](https://github.com/Sheldon04/CABM-pytorch) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.06454-b31b1b.svg)](http://arxiv.org/abs/2304.06454) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=SsFDQwPzQH0) |
| Blind Video Deflickering by Neural Filtering With a Flawed Atlas | [![GitHub Page](https://img.shields.io/badge/GitHub-Page-159957.svg)](https://chenyanglei.github.io/deflicker/) <br /> [![GitHub](https://img.shields.io/github/stars/ChenyangLEI/All-In-One-Deflicker?style=flat)](https://github.com/ChenyangLEI/All-In-One-Deflicker) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.08120-b31b1b.svg)](http://arxiv.org/abs/2303.08120) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=2TsQncdMHjE) |
| Efficient and Explicit Modelling of Image Hierarchies for Image Restoration | | | |
| Learning Distortion Invariant Representation for Image Restoration from a Causality Perspective | | | |
| Human Guided Ground-truth Generation for Realistic Image Super-Resolution | | | |
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