Learning to Translate Noise for Robust Image Denoising

Inju Ha, Donghun Ryou, Seonguk Seo, Bohyung Han

Keywords: denoising, generalization, robustness, noise translation

TL;DR: We developed a noise translation framework that transforms real-world noise into Gaussian noise for easier removal, enhancing the robustness of image denoising process.

Abstract: Image denoising techniques based on deep learning often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach translates complex, unknown real-world noise into Gaussian noise, which is spatially uncorrelated and independent of image content, through a noise translation network. The translated noisy images are then processed by an image denoising network pretrained to effectively remove Gaussian noise, enabling robust and consistent denoising performance. We also design well-motivated loss functions and architectures for the noise translation network by leveraging the mathematical properties of Gaussian noise. Experimental results demonstrate that the proposed method substantially improves robustness and generalizability, outperforming state-of-the-art methods across diverse benchmarks.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1785