Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns

Abstract

Camera sensors have color filters arranged in a mosaic layout, traditionally following the Bayer pattern. Demosaicing is a critical step camera hardware applies to obtain a full-channel RGB image. Many smartphones now have multiple sensors with different patterns, such as Quad-Bayer or Nona-Bayer. Most modern deep network-based models perform joint demosaicing and denoising with the strategy of training a separate network per pattern. Relying on individual models per pattern requires additional memory overhead and makes it challenging to switch quickly between cameras. In this work, we are interested in analyzing strategies for joint demosaicing and denoising for the three main mosaic layouts (1x1 Single-Bayer, 2x2 Quad-Bayer, and 3x3 Nona-Bayer). We found concatenating a three-channel mosaic embedding to the input image and training a unified demosaicing architecture yields results that outperform existing Quad-Bayer and Nona-Bayer models and are comparable to Single-Bayer models. Additionally, we describe a maskout strategy that enhances the model performance and facilitates dead pixel correction -- a step often overlooked by existing AI-based demosaicing models. As part of this effort, we captured a new demosaicing dataset of 638 RAW images that contain challenging scenes with patches annotated for training, validation, and testing. Code and data is available at https://github.com/SamsungLabs/unified-demosaicing.