: Our end-to-end system reconstructs images and jointly accounts for demosaicking, denoising, deconvolution, and missing data reconstruction. Thanks to the separation of image model and formulation based on natural-image priors, we support both conventional and unconventional sensor designs. Examples  Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data. We propose an end-to-end system that is aware of the camera and image model, enforces naturalimage priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation (e.g., YUV, DCT). Our system is flexible and we demonstrate it on regular Bayer images as well as images from custom sensors. In all cases, we achieve large improvements in image quality and signal reconstruction compared to state-of-the-art techniques. Finally, we show that our approach is capable of very efficiently handling high-resolution images, making even mobile implementations feasible.
Heide et al. REF developed a framework to perform common image processing steps in an ISP based on the natural-image priors.
FlexISP: a flexible camera image processing framework
TOGS
Computer Science