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: this https URL

我们提出了一种新颖的方法来增强在次优照明条件下捕获的图像，通过学习估计和校正颜色偏移，针对过曝和欠曝区域进行独立的颜色偏移纠正和调节，实现对图像色调的恢复。