Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training
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Abstract (arXiv)
As multimedia content often contains noise from intrinsic defects of digital devices, image denoising is an important step for high-level vision recognition tasks. Although several studies have developed the denoising field employing advanced Transformers, these networks are too momory-intensive for real-world applications. Additionally, there is a lack of research on lightweight denosing (LWDN) with Transformers. To handle this, this work provides seven comparative baseline Transformers for LWDN, serving as a foundation for future research. We also demonstrate the parts of randomly cropped patches significantly affect the denoising performances during training. While previous studies have overlooked this aspect, we aim to train our baseline Transformers in a truly fair manner. Furthermore, we conduct empirical analyses of various components to determine the key considerations for constructing LWDN Transformers. Codes are available at https://github.com/rami0205/LWDN.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 59 of its 86 references that are in the map. First posted on arXiv 4 Apr 2023.
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59 of 86 references.
References · 59
- 2017Attention Is All You Needcited by 59,932
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
38 more
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
- 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
- 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
- 2020Pre-Trained Image Processing Transformercited by 891
- 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
- 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
- 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
- 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 407
- 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
- 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
- 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
- 2017MemNet: A Persistent Memory Network for Image Restorationcited by 244
- 2019Residual Non-local Attention Networks for Image Restorationcited by 236
- 2019Lightweight Image Super-Resolution with Information Multi-distillation Networkcited by 227
- 2020Never Give Up: Learning Directed Exploration Strategiescited by 210
- 2018Residual Dense Network for Image Restorationcited by 181
- 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
- 2020Interpreting Super-Resolution Networks with Local Attribution Mapscited by 136
- 2019Deep Learning on Image Denoising: An overviewcited by 128
- 2020Residual Feature Distillation Network for Lightweight Image Super-Resolutioncited by 122
- 2018Fast and Accurate Single Image Super-Resolution via Information Distillation Networkcited by 121
- 2017Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutionscited by 93
- 2022Cross Aggregation Transformer for Image Restorationcited by 92
- 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
- 2020Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gapcited by 50
- 2020PAMS: Quantized Super-Resolution via Parameterized Max Scalecited by 46
- 2022N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolutioncited by 46
- 2022Multi-stage image denoising with the wavelet transformcited by 37
- 2021Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Searchcited by 34
- 2022Revisiting RCAN: Improved Training for Image Super-Resolutioncited by 32
- 2020DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networkscited by 29
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