Convolutional sparse coding for demosaicking with panchromatic pixels

Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoi...

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Bibliographic Details
Published in:Signal processing. Image communication Vol. 77; pp. 20 - 27
Main Authors: Bai, Chenyan, Li, Jia
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.09.2019
Elsevier BV
Subjects:
Alternating direction method of multipliers (ADMM)
Color coding
Color filter array (CFA)
Color imagery
Convolutional sparse coding (CSC)
Demosaicking
Digital cameras
Digital imaging
Feature maps
Image coding
Image reconstruction
Incident light
Panchromatic pixels
Pixels
Regularization
Convolutional sparse coding (CSC)
Panchromatic pixels
Demosaicking
Color filter array (CFA)
Alternating direction method of multipliers (ADMM)
ISSN:0923-5965, 1879-2677
Online Access:Get full text
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Summary:Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs’ color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality. •A convolutional sparse coding method for demosaicking panchromatic pixels is proposed•A regularization term to penalize gray filters is added•Colored filters facilitate the reconstruction of color images
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ISSN:0923-5965
1879-2677
DOI:10.1016/j.image.2019.05.018