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computer-vision-1

Analog Video Restoration

3260 papers • 126 benchmarks • 313 datasets

Restoration of analog videos exhibiting artifacts that are peculiar to digitized analog videotapes such as tape mistracking, VHS edge waving, chroma loss, tape noise etc..

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Most implemented papers

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Chen Change Loy, Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu•Mon Apr 26 2021

This study redesigns BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment, and shows that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively.

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Recurrent Video Restoration Transformer with Guided Deformable Attention

K. Zhang, L. Gool, Eddy Ilg, Radu Timofte, Jingyun Liang, Simon Green, Jiezhang Cao, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan•Sat Jun 04 2022

Extensive experiments on video super-resolution, deblurring, and denoising show that the proposed RVRT achieves state-of-the-art performance on benchmark datasets with balanced model size, testing memory and runtime.

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Reference-based Restoration of Digitized Analog Videotapes

A. Bimbo, L. Galteri, Lorenzo Agnolucci, Marco Bertini•Thu Oct 19 2023

This work uses CLIP for zero-shot artifact detection to identify the cleanest frames of each video through textual prompts describing different artifacts, and designs a transformer-based Swin-UNet network that exploits both neighboring and reference frames via the authors' Multi-Reference Spatial Feature Fusion (MRSFF) blocks.

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Memory-Augmented Non-Local Attention for Video Super-Resolution

Tao Mei, Liefeng Bo, Ji-yang Yu, Jingen Liu•Tue Aug 24 2021

This paper designs a cross-frame non-local attention mechanism that allows video superresolution without frame alignment, leading to being more robust to large motions in the video, and designs a novel memory-augmented attention module to memorize general video details during the superresolution training.

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Bringing Old Films Back to Life

Dongdong Chen, Jing Liao, Bo Zhang, Ziyu Wan•Wed Mar 30 2022

A learning-based framework, recurrent transformer network (RTN), to restore heavily degraded old films, based on the hidden knowledge learned from adjacent frames that contain abundant information about the occlusion, which is beneficial to restore challenging artifacts of each frame while ensuring temporal coherency.

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Multi-Scale Memory-Based Video Deblurring

Angela Yao, Bongil Ji•Tue Apr 05 2022

A memory branch is designed to memorize the blurry-sharp feature pairs in the memory bank, thus providing useful information for the blurry query input in order to achieve fine-grained deblurring.

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Restoration of Analog Videos Using Swin-UNet

A. Bimbo, L. Galteri, Lorenzo Agnolucci, Marco Bertini•Sun Oct 09 2022

This paper presents a system to restore analog videos of historical archives that uses a multi-frame approach and is able to deal also with severe tape mistracking, which results in completely scrambled frames.

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Analog Video Restoration | State-of-the-Art