DePhaseNet: A deep convolutional network using phase differentiated layers and frequency based custom loss for RGBW image sensor demosaicing

Abstract

Panchromatic Color Filter Arrays with white signal were introduced a while ago, such as RGBW Kodak (CFA2.0) array, assuming to have better resolution in lowlight due to panchromatic signal. However, there is no successful RGBW image sensor in the industry targeting mobile cameras until now. In this work, we introduce a novel Samsung RGBW image sensor and we study its performance in a popular remosaic scenario. We propose a DePhaseNet - a deep fully convolutional network to solve RGBW remosaicing or demosaicing problem. We propose to have 3 layers of phase differentiated inputs and custom frequency based loss function for each layer. Proposed method successfully suppress False Colors inherent to RGBW sensor due to heavily under-sampled colors. By using this method, we were able not only to increase details preservation, but also increased color reproduction by 2% over conventional method. We found that RGBW sensor is beneficial not only in low light scenarios, but also in widely spread remosaic scenarios. Experiments show improvement in image quality, yielding CPSNR of 42dB for Kodak dataset, reaching the bar of Bayer CFA demosaicing result. Proposed method advances state-of-the-art in RGBW demosaic, by 6dB in CPSNR.