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