Exposure Correction Model to Enhance Image Quality
This addresses image quality issues for photography and computer vision applications, but it is incremental as it builds on existing exposure correction and generative model techniques.
The paper tackles the problem of exposure errors in images, which degrade contrast and visibility, by proposing an end-to-end exposure correction model that handles both under- and overexposure with a single model, achieving state-of-the-art results on a large-scale dataset and significantly improving portrait matting quality after correction.
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