mlsanityData to 7 Oct 2026

Blind2Sound: Self-Supervised Image Denoising without Residual Noise

arXiv 2303.05183 ↗First posted 9 Mar 2023cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 9 Mar 2023.

Builds on

24 of 52 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  5. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  6. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  7. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  8. 2020Learning Enriched Features for Real Image Restoration and Enhancementcited by 311
  9. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  10. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
  11. 2018Unprocessing Images for Learned Raw Denoisingcited by 250
  12. 2020CycleISP: Real Image Restoration via Improved Data Synthesiscited by 212
12 more
  1. 2019Real Image Denoising with Feature Attentioncited by 176
  2. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
  3. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  4. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125
  5. 2019Variational Denoising Network: Toward Blind Noise Modeling and Removalcited by 117
  6. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 92
  7. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 83
  8. 2020Unpaired Learning of Deep Image Denoisingcited by 74
  9. 2018A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Imagescited by 70
  10. 2022CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Imagecited by 45
  11. 2021FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noisecited by 22
  12. 2018Fully Convolutional Pixel Adaptive Image Denoisercited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoisingcited by 1