LOW-LIGHT IMAGE ENHANCEMENT ALGORITHM BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS
Because of the low contrast and signal-to-noise ratio of low-illuminance images,traditional image enhancement algorithms can easily cause noise amplification while improving image contrast.In view of this,a low-light image enhancement algorithm based on robust principal component analysis(RPCA)is proposed.The algorithm decomposed the image into illuminance component and reflection component based on Retinex theory,and then used gamma correct to enhance the illuminance component.The enhanced illumination component and reflection component were combined into the final enhanced image.The image decomposition was realized by the RPCA method,because this method could effectively separate the illuminance information from the noise,so as to avoid amplifying the noise when the illuminance component was enhanced.In order to improve the computational efficiency,the algorithm used the inexact augmented Lagrange multiplier(IALM)method to solve the RPCA decomposition problem.Experimental results show that the algorithm avoids amplification noise while enhancing image contrast,and its subjective evaluation and objective indicators are better than several classic image enhancement algorithms,with better visual effects and lower computational complexity.
Image enhancementLow-light imageRetinex theoryRobust principal component analysis
万方数据
维普
