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
翻译:图像中的曝光错误导致对比度的退化, 内容的可见度低。 在本文中, 我们解决这个问题, 并提议一个端到端的曝光纠正模型, 以便用单一模型处理曝光不足和过度的错误。 我们的模型包含一个图像编码器、 连续的剩余区块和图像解码器, 以合成被校正的图像。 我们使用感知丢失、 特征匹配丢失和多尺度的辨别器来提高生成图像的质量, 并使培训更加稳定。 实验结果显示了拟议模型的有效性。 我们在大规模曝光数据集上实现了最新的结果。 此外, 我们调查了图像的曝光设置对肖像配对任务的影响。 我们发现, 曝光不足和过度的图像在肖像配对模型的性能中造成了严重退化。 我们显示, 在应用了曝光校正后, 肖像配方质量显著提高 。 https://github.com/ Yamind16/Exposureure Correction