End-to-End Denoising of Dark Image Using Residual Dense Network

Di Zhao, Lan Ma, Zhixian Lin, Tailiang Guo, Shanling Lin
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Abstract

When taking pictures in extremely dark light, the image taken is usually very dark and noisy due to the small amount of light entering, and there is clear distortion of color. Since the method of using multi-frame or long-exposure has limitations, we use the single-frame denoising method based on residual dense network (RDN), which uses residual dense block (RDB) to makes full use of local features. Our method has achieved better results than state-of-the-art methods. In addition, we have applied the trained model without fine-tuning on photos captured by different cameras and have obtained similar end-to-end enhancements.
基于残差密集网络的端到端暗图像去噪
在极暗的光线下拍摄时,由于进入的光线很少,拍摄的图像通常非常黑暗和嘈杂,并且有明显的色彩失真。由于使用多帧或长曝光的方法存在局限性,我们采用基于残差密集网络(RDN)的单帧去噪方法,该方法利用残差密集块(RDB)来充分利用局部特征。我们的方法比最先进的方法取得了更好的效果。此外,我们将未经微调的训练模型应用于不同相机拍摄的照片,并获得了类似的端到端增强。
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