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arXiv · 2310.00894

JPEG Information Regularized Deep Image Prior for Denoising

Tsukasa Takagi·Shinya Ishizaki·Shin-ichi Maeda
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Abstract

Image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.

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Tsukasa Takagi, Shinya Ishizaki, Shin-ichi Maeda. 2023-10-02. JPEG Information Regularized Deep Image Prior for Denoising. https://doi.org/10.1109/icip49359.2023.10223088

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