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Modern digital cameras and smartphones mostly rely on image signal processing(ISP) pipelines to produce realistic colored RGB images. However, compared toDSLR cameras, low-quality images are usually obtained in many portable mobiledevices with compact camera sensors due to their physical limitations. Thelow-quality images have multiple degradations i.e., sub-pixel shift due tocamera motion, mosaick patterns due to camera color filter array,low-resolution due to smaller camera sensors, and the rest information arecorrupted by the noise. Such degradations limit the performance of currentSingle Image Super-resolution (SISR) methods in recovering high-resolution (HR)image details from a single low-resolution (LR) image. In this work, we proposea Raw Burst Super-Resolution Iterative Convolutional Neural Network (RBSRICNN)that follows the burst photography pipeline as a whole by a forward (physical)model. The proposed Burst SR scheme solves the problem with classical imageregularization, convex optimization, and deep learning techniques, compared toexisting black-box data-driven methods. The proposed network produces the finaloutput by an iterative refinement of the intermediate SR estimates. Wedemonstrate the effectiveness of our proposed approach in quantitative andqualitative experiments that generalize robustly to real LR burst inputs withonl synthetic burst data available for training.

RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network