[1]TAO Linmi,YUAN Chun,WANG Haoda.Reconstruction method for restoring the natural color of an image[J].CAAI Transactions on Intelligent Systems,2019,14(5):877-881.[doi:10.11992/tis.201805011]
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Reconstruction method for restoring the natural color of an image

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 877-881 Column: 学术论文—机器学习 Public date: 2019-09-05
Title:
Reconstruction method for restoring the natural color of an image
Author(s):
TAO Linmi;  YUAN Chun;  WANG Haoda
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
color filter array; compressed sensing; sparse coding; dictionary learning; image reconstruction; natural color
CLC:
TP18
DOI:
10.11992/tis.201805011
Abstract:
Currently, the digital camera captures one color component at each pixel location through a color filter array and reconstructs a full-color digital image. The compressed sensing theory has proven that this reconstruction is based on the error bound with sparse coding dictionary. However, in practice, there is a hidden problem:the sparse coding dictionary needed for image reconstruction is learned from the image database while the current digital images are reconstructed. Thus, there is a cyclic paradox of reconstructing images from the reconstructed image learning dictionary. To solve this problem, this study proposes and constructs a fully sampled color image dataset called Sandwich to break the cyclic paradox of compressed sensing theory in the application of image reconstruction, enabling the compressed sensing method to reconstruct truly natural color images. The dictionary trained from the constructed Sandwich dataset can be applied to several domains, such as image super-resolution reconstruction, denoising, and restoration. The in-depth image reconstruction experiments show that the dictionary trained using the Sandwich image dataset is better than the dictionary trained using the traditional dataset in terms of both dictionary atomic characteristics and the reconstructed image quality.
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