Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by the camera sensor, the process of demosaicing is inherently ill-posed. The presence of noise further exacerbates this problem. Performing these two steps sequentially may distort the content of the captured RAW images and accumulate errors from one step to another. Recent deep neural-network-based approaches have shown the effectiveness of joint demosaicing and denoising to mitigate such challenges. However, these methods typically require a large number of training samples and do not generalize well to different types and intensities of noise. In this paper, we propose a novel joint demosaicing and denoising method, dubbed JDD-DoubleDIP, which operates directly on a single RAW image without requiring any training data. We validate the effectiveness of our method on two popular datasets -- Kodak and McMaster -- with various noises and noise intensities. The experimental results show that our method consistently outperforms other compared methods in terms of PSNR, SSIM, and qualitative visual perception.

翻译：RAW图像的去马赛克与去噪是现代数码相机处理流程中的关键环节。由于相机传感器仅捕获生成数字图像所需的三分之一色彩信息，去马赛克过程本质上存在病态性，而噪声的存在进一步加剧了该问题。若按顺序执行这两个步骤，可能会扭曲所捕获RAW图像的内容，并在步骤间累积误差。近期基于深度神经网络的方法已证明联合去马赛克与去噪能有效缓解此类挑战。然而，这些方法通常需要大量训练样本，且难以泛化至不同类型和强度的噪声。本文提出一种名为JDD-DoubleDIP的新型联合去马赛克与去噪方法，该方法可直接对单张RAW图像进行处理，无需任何训练数据。我们在Kodak和McMaster两个主流数据集上验证了该方法在不同噪声类型与强度下的有效性。实验结果表明，在PSNR、SSIM及定性视觉感知方面，本方法始终优于其他对比方法。