Deep Quantigraphic Image Enhancement via Comparametric Equations
- URL: http://arxiv.org/abs/2304.02285v1
- Date: Wed, 5 Apr 2023 08:14:41 GMT
- Title: Deep Quantigraphic Image Enhancement via Comparametric Equations
- Authors: Xiaomeng Wu, Yongqing Sun, Akisato Kimura
- Abstract summary: We propose a novel trainable module that diversifies the conversion from the low-light image and illumination map to the enhanced image.
Our method improves the flexibility of deep image enhancement, limits the computational burden to illumination estimation, and allows for fully unsupervised learning adaptable to the diverse demands of different tasks.
- Score: 15.782217616496055
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most recent methods of deep image enhancement can be generally classified
into two types: decompose-and-enhance and illumination estimation-centric. The
former is usually less efficient, and the latter is constrained by a strong
assumption regarding image reflectance as the desired enhancement result. To
alleviate this constraint while retaining high efficiency, we propose a novel
trainable module that diversifies the conversion from the low-light image and
illumination map to the enhanced image. It formulates image enhancement as a
comparametric equation parameterized by a camera response function and an
exposure compensation ratio. By incorporating this module in an illumination
estimation-centric DNN, our method improves the flexibility of deep image
enhancement, limits the computational burden to illumination estimation, and
allows for fully unsupervised learning adaptable to the diverse demands of
different tasks.
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