论文标题
曝光校正模型以增强图像质量
Exposure Correction Model to Enhance Image Quality
论文作者
论文摘要
图像中的暴露误差会导致对比度下降，并且内容的可见性较低。在本文中，我们解决了此问题，并提出了一个端到端的曝光校正模型，以便使用单个模型处理不足和过度暴露错误。我们的模型包含一个图像编码器，连续的残留块和图像解码器，以合成校正的图像。我们利用感知损失，功能匹配损失和多尺度判别器来提高生成图像的质量，并使训练更稳定。实验结果表明提出的模型的有效性。我们在大规模曝光数据集上实现了最先进的结果。此外，我们研究了图像设置对肖像效果任务的影响。我们发现，在肖像床位模型的性能中，不足和曝光过度的图像会导致严重的降解。我们表明，在使用所提出的模型应用暴露校正后，肖像垫的质量大大增加。 https://github.com/yamand16/exposurecorrection
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
