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Controllable Confidence-Based Image Denoising

arXiv 2106.09311 ↗First posted 17 Jun 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

Image denoising is a classic restoration problem. Yet, current deep learning methods are subject to the problems of generalization and interpretability. To mitigate these problems, in this project, we present a framework that is capable of controllable, confidence-based noise removal. The framework is based on the fusion between two different denoised images, both derived from the same noisy input. One of the two is denoised using generic algorithms (e.g. Gaussian), which make few assumptions on the input images, therefore, generalize in all scenarios. The other is denoised using deep learning, performing well on seen datasets. We introduce a set of techniques to fuse the two components smoothly in the frequency domain. Beyond that, we estimate the confidence of a deep learning denoiser to allow users to interpret the output, and provide a fusion strategy that safeguards them against out-of-distribution inputs. Through experiments, we demonstrate the effectiveness of the proposed framework in different use cases.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 17 Jun 2021.

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22 of 34 references.

References · 22

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  4. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  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. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  8. 2017Learning a Single Convolutional Super-Resolution Network for Multiple Degradationscited by 227
  9. 2019Blind Super-Resolution With Iterative Kernel Correctioncited by 203
  10. 2018Multi-level Wavelet-CNN for Image Restorationcited by 184
  11. 2019Real Image Denoising with Feature Attentioncited by 176
  12. 2019Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Rainingcited by 117
10 more
  1. 2020Confidence-Aware Learning for Deep Neural Networkscited by 113
  2. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
  3. 2020Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalizationcited by 92
  4. 2018A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Imagescited by 70
  5. 2019Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learningcited by 31
  6. 2020Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networkscited by 21
  7. 2020BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restorationcited by 15
  8. 2019Hierarchical Back Projection Network for Image Super-Resolutioncited by 12
  9. 2018Correction by Projection: Denoising Images with Generative Adversarial Networkscited by 11
  10. 2019Confidence Measure Guided Single Image De-rainingcited by 10

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