研究目的
To propose a detail preserving variational model for Retinex to estimate illumination and reflectance from an observed image without using log-transform, which causes loss of details.
研究成果
The proposed variational model effectively decomposes images into reflectance and illumination without log-transform, preserving details better than existing methods. It shows competitive performance in subjective and objective assessments, with faster convergence and good computational efficiency.
研究不足
The assumption that illumination is smooth may not hold for images with shadows; noise suppression is limited; the method is sensitive to Gamma correction parameters.
1:Experimental Design and Method Selection:
The study uses a variational model with constraints on reflectance and illumination, solved using the alternating direction method of multipliers (ADMM).
2:Sample Selection and Data Sources:
Test images include RGB images transformed to HSV space, with V-channels used as input. Datasets include non-uniform illumination image dataset, Berkeley Segmentation dataset, and Pku-EAQA dataset.
3:List of Experimental Equipment and Materials:
A PC with Intel Core i7 CPU 6800k, 64GB RAM, and MATLAB software.
4:Experimental Procedures and Operational Workflow:
The algorithm initializes parameters, iteratively updates variables (R, L, u, v, d, q) using ADMM, and stops based on relative error criteria. Gamma correction is applied for image enhancement.
5:Data Analysis Methods:
Performance is evaluated using Mean-Square Error (MSE), Natural Image Quality Evaluator (NIQE), and Signal-to-Noise Ratio (SNR).
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