2000 character limit reached
Masked and Shuffled Blind Spot Denoising for Real-World Images
Published 15 Apr 2024 in cs.CV and cs.LG | (2404.09389v1)
Abstract: We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the input and the (unknown) noise correlation. Moreover, we introduce a shuffling technique to weaken the local correlation of noise, which in turn yields an additional denoising performance improvement. We evaluate MASH via extensive experiments on real-world noisy image datasets. We demonstrate on par or better results compared to existing self-supervised denoising methods.
- A high-quality denoising dataset for smartphone cameras. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
- Jian-Feng Cai Bao, Chenglong and Hui Ji. Fast sparsity-based orthogonal dictionary learning for image restoration. In Proceedings of the IEEE International Conference on Computer Vision, 2013.
- Non-local means denoising. In Image Processing On Line 1, 2011.
- Image denoising: Can plain neural networks compete with bm3d? In 2012 IEEE conference on computer vision and pattern recognition, pages 2392–2399. IEEE, 2012.
- Score priors guided deep variational inference for unsupervised real-world single image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12937–12948, 2023.
- Bm3d image denoising with shape-adaptive principal component analysis. In SPARS’09-Signal Processing with Adaptive Sparse Structured Representations, 2009.
- Rich Franzen. Kodak lossless true color image suite. source: http://r0k. us/graphics/kodak, 4(2):9, 1999.
- C2n: Practical generative noise modeling for real-world denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2350–2359, 2021.
- A brief review of image denoising algorithms and beyond. Inpainting and Denoising Challenges, pages 1–21, 2019.
- Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000–16009, 2022.
- Neighbor2neighbor: Self-supervised denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14781–14790, 2021.
- Noise2noise: Learning image restoration without clean data. In arXiv preprint arXiv:1803.04189, 2018.
- Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019.
- Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17725–17734, 2022.
- Spatially adaptive self-supervised learning for real-world image denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9914–9924, 2023.
- Image denoising via sparse and redundant representations over learned dictionaries. In IEEE Transactions on Image processing 15.12, 2006.
- A holistic approach to cross-channel image noise modeling and its application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1683–1691, 2016.
- Cvf-sid: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17583–17591, 2022.
- Self2self with dropout: Learning self-supervised denoising from single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1890–1898, 2020.
- Weighted nuclear norm minimization with application to image denoising. In In Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.
- Modeling srgb camera noise with normalizing flows. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17463–17471, 2022.
- Global image denoising. IEEE Transactions on Image Processing, 23(2):755–768, 2013.
- State-of-art analysis of image denoising methods using convolutional neural networks. IET Image Processing, 13(13):2367–2380, 2019.
- Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.
- Blind2unblind: Self-supervised image denoising with visible blind spots. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2027–2036, 2022.
- Noise2same: Optimizing a self-supervised bound for image denoising. In Advances in Neural Information Processing Systems 33, 2020.
- Two-stage image denoising by principal component analysis with local pixel grouping. Pattern recognition, 43(4):1531–1549, 2010.
- A poisson-gaussian denoising dataset with real fluorescence microscopy images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11710–11718, 2019.
- An unsupervised deep learning approach for real-world image denoising. In International Conference on Learning Representations, 2020.
- Learn from unpaired data for image restoration: A variational bayes approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5889–5903, 2022.
- When awgn-based denoiser meets real noises. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 13074–13081, 2020.
- Separating signal from noise using patch recurrence across scales. In proceedings of the IEEE conference on computer vision and pattern recognition, pages 1195–1202, 2013.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.