Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding

Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been po...

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Vydáno v:Applied sciences Ročník 10; číslo 12; s. 4395
Hlavní autoři: Yoon, Jongsu, Choe, Yoonsik
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2020
Témata:
Algorithms
Cerebral cortex
Comparative analysis
Convolutional codes
convolutional sparse coding
Decomposition
Dictionaries
human visual system
image enhancement
Image processing
Innovations
Machine vision
Retinex
Sparsity
South Korea
ISSN:2076-3417, 2076-3417
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Shrnutí:Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement.
Bibliografie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10124395