Abstract
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.
Highlights
The color of an object determined by a machine visual system (MVS) such as a digital camera is based on the amount of light reflected from it
We propose an image enhancement method based on Retinex theory using convolutional sparse coding (CSC)
We proposed Retinex based image enhancement method via CSC
Summary
The color of an object determined by a machine visual system (MVS) such as a digital camera is based on the amount of light reflected from it. Owing to the inconsistency between the HVS and the MVS under various illumination conditions, a machine cannot obtain the same image as a human These inconsistencies cause algorithmic errors in functions such as color separation, pattern recognition and object tracking. Sparse coding is a method of constructing the basis of an image through a dictionary Such conventional Retinex methods have a problem in that when the illumination changes rapidly, details in a complex area within the image are not properly reflected and blurred, or illumination and reflectance cannot be accurately decomposed. We propose an image enhancement method based on Retinex theory using convolutional sparse coding (CSC). In CSC to construct a more compact dictionary in limited memory Since it is a form of reflectance basis of a general image, it only has additional information that fits the basis.
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