Joint Demosaicing and Denoising via Learned Non-parametric Random Fields

We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: first, it needs to model and respect the statistics of natural images in order to reconstruct natural looking images; second, it needs to be able to perform well in the presence of noise. To facilitate an objective assessment of current methods we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1. the model is trained to directly optimize a user-specified performance measure such as peak signal-to-noise ratio (PSNR) or structural similarity (SSIM), 2. we can handle novel color filter array layouts by retraining the model on such layouts, 3. it outperforms the previous state-of-the-art, in some setups by 0.7dB PSNR, faithfully reconstructing edges, textures, and smooth areas. Our results demonstrate that in demosaicing and related imaging applications, discriminatively trained machine learning models have the potential for peak performance at comparatively low engineering effort.