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NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challe...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liang, Jie Timofte, Radu Yi, Qiaosi Liu, Shuaizheng Sun, Lingchen Wu, Rongyuan Zhang, Xindong Zeng, Hui Zhang, Lei Huang, Yibin Liu, Shuai Li, Yongqiang Feng, Chaoyu Wang, Xiaotao Lei, Lei Chen, Yuxiang Chen, Xiangyu Chen, Qiubo Sun, Fengyu Cui, Mengying Chen, Jiaxu Hu, Zhenyu Liu, Jingyun Ma, Wenzhuo Wang, Ce Zheng, Hanyou Sun, Wanjie Chen, Zhenzhong Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjolund, Jens Schon, Thomas B. Dun, Xiong Ji, Pengzhou Xing, Yujie Wang, Xuquan Wang, Zhanshan Cheng, Xinbin Xiao, Jun He, Chenhang Wang, Xiuyuan Liu, Zhi-Song Miao, Zimeng Yin, Zhicun Liu, Ming Zuo, Wangmeng Li, Shuai Uppsala Univ Dept Informat Technol Uppsala Sweden Tongji Univ Inst Precis Opt Engn Sch Phys Sci & Engn Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Lappeenranta Lahti Univ Technol Lappeenranta Finland Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9798350365474

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https://drive. ***/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https://***/competitions/17632.

关键词： HTTP

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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution 31

New Techniques for Preserving Global Structure and Denoising...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bei, Yijie Damian, Alex Hu, Shijia Menon, Sachit Ravi, Nikhil Rudin, Cynthia Duke Univ Durham NC 27706 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.

关键词： Image resolution Noise reduction Training Task analysis Neural networks computer architecture Knowledge engineering

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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Subjective Quality Optimized Efficient Image Compression

Subjective Quality Optimized Efficient Image Compression

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Xining Chen, Tong Ma, Zhan Nanjing Univ Vis Lab Nanjing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose an efficient image compression framework that is optimized for subjective quality. Our framework is mainly based on the NLAIC (NonLocal Attention Optimized Image Coding) model which applied Variational Autoencoder (VAE) and non-local attention module to end-to-end image compression. This work makes two major contributions to the NLAIC framework. First, our models are optimized for subjective-friendly loss functions rather than conventional MSE (Mean Squared Error) or MS-SSIM (Multiscale Structural Similarity) which was widely used in previous works. Second, we introduce block-based inference mechanism to reduce the running memory consumption of the image compression network, and suggest a partial post-processing step to alleviate block artifacts caused by block-based inference in a lightweight computational fashion. Experiments have proved that the image reconstructed by our method can preserve more texture details than models trained for optimal MSE or MS-SSIM and also present capability for high-throughput decoding.

关键词： Measurement computer vision Image coding Inference mechanisms conferences Memory management Distortion

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Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

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Image Reconstruction from Neuromorphic Event Cameras using Laplacian-Prediction and Poisson Integration with Spiking and Artificial Neural Networks

Image Reconstruction from Neuromorphic Event Cameras using L...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Duwek, Hadar Cohen Shalumov, Albert Tsur, Elishai Ezra Open Univ Israel Neurobiomorph Engn Lab NBEL Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665448994

Event cameras are robust neuromorphic visual sensors, which communicate transients in luminance as events. Current paradigm for image reconstruction from event data relies on direct optimization of artificial Convolutional Neural Networks (CNNs). Here we proposed a two-phase neural network, which comprises a CNN, optimized for Laplacian prediction followed by a Spiking Neural Network (SNN) optimized for Poisson integration. By introducing Laplacian prediction into the pipeline, we provide image reconstruction with a network comprising only 200 parameters. We converted the CNN to SNN, providing a full neuromorphic implementation. We further optimized the network with Mish activation and a novel convoluted CNN design, proposing a hybrid of spiking and artificial neural network with < 100 parameters. Models were evaluated on both N-MNIST and N-Caltech101 datasets.

关键词： Visualization Laplace equations Neuromorphics Pipelines Cameras Sensors pattern recognition

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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CompConv: A Compact Convolution Module for Efficient Feature Learning

CompConv: A Compact Convolution Module for Efficient Feature...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Chen Xu, Yinghao Shen, Yujun Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Furthermore, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.

关键词： computer vision Convolution Computational modeling conferences Benchmark testing pattern recognition Computational efficiency

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CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Mirzadeh, Seyed Iman Ghasemzadeh, Hassan Washington State Univ Pullman WA 99164 USA

ISBN: (纸本)9781665448994

Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need for a library that facilitates both research and development in this field is more visible than ever. However, CL algorithms' codes are currently scattered over isolated repositories written with different frameworks, making it difficult for researchers and practitioners to work with various CL algorithms and benchmarks using the same interface. In this paper, we introduce CL-Gym, a full-featured continual learning library that overcomes this challenge and accelerates the research and development. In addition to the necessary infrastructure for running end-to-end continual learning experiments, CL-Gym includes benchmarks for various CL scenarios and several state-of-the-art CL algorithms. In this paper, we present the architecture, design philosophies, and technical details behind CL-Gym (1).

关键词： computer vision Philosophical considerations conferences computer architecture Learning (artificial intelligence) Benchmark testing Libraries

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