Towards Robust Low Light Image Enhancement

• URL: http://arxiv.org/abs/2205.08615v1
• Date: Tue, 17 May 2022 20:14:18 GMT
• Title: Towards Robust Low Light Image Enhancement
• Authors: Sara Aghajanzadeh and David Forsyth
• Abstract summary: We study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We use a supervised learning method, relying on a straightforward simulation of an imaging pipeline to generate usable dataset for training and testing.
• Score: 6.85316573653194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We don't know the true camera reponse function for such images and they are not RAW. We use a supervised learning method, relying on a straightforward simulation of an imaging pipeline to generate usable dataset for training and testing. On a number of standard datasets, our approach outperforms the state of the art quantitatively. Qualitative comparisons suggest strong improvements in reconstruction accuracy.

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