Joint Demosaicing and Denoising with Double Deep Image Priors
Taihui Li Anish Lahiri Yutong Dai Owen Mayer
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.