Controllable Confidence-Based Image Denoising

• URL: http://arxiv.org/abs/2106.09311v1
• Date: Thu, 17 Jun 2021 08:25:12 GMT
• Title: Controllable Confidence-Based Image Denoising
• Authors: Haley Owsianko, Florian Cassayre and Qiyuan Liang
• Abstract summary: 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. We demonstrate the effectiveness of the proposed framework in different use cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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