Color Shift Estimation-and-Correction for Image Enhancement

• URL: http://arxiv.org/abs/2405.17725v2
• Date: Wed, 29 May 2024 10:03:06 GMT
• Title: Color Shift Estimation-and-Correction for Image Enhancement
• Authors: Yiyu Li, Ke Xu, Gerhard Petrus Hancke, Rynson W. H. Lau,
• Abstract summary: 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. We propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts.
• Score: 37.52492067462557
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project webpage: https://github.com/yiyulics/CSEC.

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