Deep Residual Network for Joint Demosaicing and Super-Resolution
This work addresses image quality issues in digital photography for applications like photography and computer vision, but it is incremental as it combines known tasks with a standard deep learning approach.
The paper tackles the problem of artifacts accumulating when demosaicing and super-resolution are performed separately on Bayer images, and proposes a deep residual network to jointly perform both tasks, achieving superior results in PSNR and SSIM metrics compared to state-of-the-art methods.
In digital photography, two image restoration tasks have been studied extensively and resolved independently: demosaicing and super-resolution. Both these tasks are related to resolution limitations of the camera. Performing super-resolution on a demosaiced images simply exacerbates the artifacts introduced by demosaicing. In this paper, we show that such accumulation of errors can be easily averted by jointly performing demosaicing and super-resolution. To this end, we propose a deep residual network for learning an end-to-end mapping between Bayer images and high-resolution images. By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately. We perform extensive experiments to show that our deep residual network achieves demosaiced and super-resolved images that are superior to the state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics.