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arXiv · 1803.01532

Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination

Chang Liu·Xiaolin Wu·Xiao Shu
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Abstract

All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems). The loss of image details due to A/D quantization is complete and it cannot be recovered by traditional image processing methods, but the modern data-driven machine learning approach offers a much needed cure to the problem. In this work we propose a novel approach to restore and enhance images acquired in low and uneven lighting. First, the ill illumination is algorithmically compensated by emulating the effects of artificial supplementary lighting. Then a DCNN trained using only synthetic data recovers the missing detail caused by quantization.

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Chang Liu, Xiaolin Wu, Xiao Shu. 2018-03-05. Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination. https://arxiv.org/abs/1803.01532

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