Exposure Correction Model to Enhance Image Quality
- URL: http://arxiv.org/abs/2204.10648v1
- Date: Fri, 22 Apr 2022 11:38:52 GMT
- Title: Exposure Correction Model to Enhance Image Quality
- Authors: Fevziye Irem Eyiokur and Dogucan Yaman and Haz{\i}m Kemal Ekenel and
Alexander Waibel
- Abstract summary: We propose an end-to-end exposure correction model to handle both under- and overexposure errors.
Our model contains an image encoder, consecutive residual blocks, and image decoder to synthesize the corrected image.
We show that after applying exposure correction with the proposed model, the portrait matting quality increases significantly.
- Score: 70.7848787073901
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Exposure errors in an image cause a degradation in the contrast and low
visibility in the content. In this paper, we address this problem and propose
an end-to-end exposure correction model in order to handle both under- and
overexposure errors with a single model. Our model contains an image encoder,
consecutive residual blocks, and image decoder to synthesize the corrected
image. We utilize perceptual loss, feature matching loss, and multi-scale
discriminator to increase the quality of the generated image as well as to make
the training more stable. The experimental results indicate the effectiveness
of proposed model. We achieve the state-of-the-art result on a large-scale
exposure dataset. Besides, we investigate the effect of exposure setting of the
image on the portrait matting task. We find that under- and overexposed images
cause severe degradation in the performance of the portrait matting models. We
show that after applying exposure correction with the proposed model, the
portrait matting quality increases significantly.
https://github.com/yamand16/ExposureCorrection
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