Back to articles
Article
Volume: 34 | Article ID: ISS-200
Image
DePhaseNet: A deep convolutional network using phase differentiated layers and frequency based custom loss for RGBW image sensor demosaicing
  DOI :  10.2352/EI.2022.34.7.ISS-200  Published OnlineJanuary 2022
Abstract
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.

Subject Areas :
Views 85
Downloads 25
 articleview.views 85
 articleview.downloads 25
  Cite this article 

Irina Kim, Youngil Seo, Dongpan Lim, Jeongguk Lee, Wooseok Choi, Seongwook Song, "DePhaseNet: A deep convolutional network using phase differentiated layers and frequency based custom loss for RGBW image sensor demosaicingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging Sensors and Systems,  2022,  pp 200-1 - 200-5,  https://doi.org/10.2352/EI.2022.34.7.ISS-200

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2022
ei
Electronic Imaging
2470-1173
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA