Pixel binning is a term that is gaining popularity lately. It consists of using high pixel density camera sensors where the pixels are grouped together when low light levels are encountered, and, in the case of bright light scenes, the pixels are not grouped together. One such pixel arrangement is Quad Bayer or Tetra. Historically, significant efforts have been dedicated to demosaicing and denoising Bayer images, yet limited consideration has been directed towards Quad Bayer sensors, owing to their recent introduction. One unique challenge in training deep learning networks for Quad Bayer images is how to encode such data (spatial vs depth arrangement). Conventionally, when training denoising networks on bayer images, the input is split in to individual color channels, however, as results would show, taking that approach in case of Quad Bayer images produces inferior quality results.In this paper, we present an efficient way of grouping the pixels of a tetra sensor that achieves the best trade off between image quality and inference speed. Due to very large number of pixels, the network training requires enormous amounts of data, making the network prone to over-fitting in case of limited data. In order to regularize the network so as to not overfit, we present a novel inter channel loss function that effectively regularizes the network training. Finally, we do an ablation study to analyze the loss function that we present, pixel grouping for tetra sensor and the proportion of input data with different amounts of noise level. Results show that the techniques presented in this paper produce denoised tetra images that are of better quality than traditional methods. We hope that this paper will inspire further research in developing algorithms for the new Quad Bayer, Hexa Deca and Nona sensors.
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