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High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering.

08:00 EDT 9th April 2019 | BioPortfolio

Summary of "High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering."

Capturing images at high ISO modes will introduce much realistic noise, which is difficult to be removed by traditional denoising methods. In this paper, we propose a novel denoising method for high ISO JPEG images via deep fusion of collaborative and convolutional filtering. Collaborative filtering explores the non-local similarity prior of natural images, while convolutional filtering takes advantage of the large capacity of convolutional neural network (CNN) to infer noise from noisy images. We observe that the noise variance map of a high ISO JPEG image is spatial-dependent and has a Bayer-like pattern. Therefore, we introduce the Bayer pattern prior in our noise estimation and collaborative filtering stages. Since collaborative filtering is good at recovering repeatable structures and convolutional filtering is good at recovering irregular patterns and removing noise in flat regions, we propose to fuse the strengths of the two methods via deep CNN. Experimental results demonstrate that our method outperforms state-of-the-art realistic noise removal methods for a wide variety of testing images in both subjective and objective measurements. In addition, we construct a dataset with noisy and clean image pairs for high ISO JPEG images to facilitate research on this topic.

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This article was published in the following journal.

Name: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
ISSN: 1941-0042
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