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Pixel binning is considered one of the most prominent solutions to tackle thehardware limitation of smartphone cameras. Despite numerous advantages, such animage sensor has to appropriate an artefact-prone non-Bayer colour filter array(CFA) to enable the binning capability. Contrarily, performing essential imagesignal processing (ISP) tasks like demosaicking and denoising, explicitly withsuch CFA patterns, makes the reconstruction process notably complicated. Inthis paper, we tackle the challenges of joint demosaicing and denoising (JDD)on such an image sensor by introducing a novel learning-based method. Theproposed method leverages the depth and spatial attention in a deep network.The proposed network is guided by a multi-term objective function, includingtwo novel perceptual losses to produce visually plausible images. On top ofthat, we stretch the proposed image processing pipeline to comprehensivelyreconstruct and enhance the images captured with a smartphone camera, whichuses pixel binning techniques. The experimental results illustrate that theproposed method can outperform the existing methods by a noticeable margin inqualitative and quantitative comparisons. Code available:https://github.com/sharif-apu/BJDD_CVPR21.

Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor