Color Shift Estimation-and-Correction for Image Enhancement
Yiyu Li, Ke Xu, Gerhard Petrus Hancke, Rynson W.H. Lau

TL;DR
This paper introduces a novel method for image enhancement that estimates and corrects color shifts in over- and under-exposed regions, leading to more accurate color restoration in images with exposure issues.
Contribution
The paper proposes a new approach using a UNet-based network, COSE and COMO modules to separately estimate and correct color shifts in over- and under-exposed regions.
Findings
Outperforms existing image enhancement methods.
Effectively restores accurate colors in challenging exposure conditions.
Demonstrates superior visual quality in experimental results.
Abstract
Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate the color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and under-exposed regions display opposite color tone distribution shifts with respect to each other, which may not be easily normalized in joint modeling as they usually do not have ``normal-exposed'' regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce…
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Taxonomy
TopicsImage Enhancement Techniques · Image and Signal Denoising Methods · Color Science and Applications
