Sparse representation and reconstruction of image based on K-SVD dictionary learning

Abstract

K-Singular Value Decomposition (K-SVD) is an excellent feature analysis method, but it has a large amount of calculation and low algorithm efficiency. To solve the above problems, a dictionary learning method for image block classification training is proposed. This method uses image entropy as the classification index of image samples, selects more complex texture image blocks with more analytical value, and uses Discrete Cosine Transform- Discrete Wavelet Transformation (DCT-DWT) dictionary to train them in K-Singular Value Decomposition. Through the feedback of sparse coefficients, the threshold value is dynamically changed and the precision of sparse coding is controlled. In the sparse solution, a variable-step Orthogonal Matching Pursuit (OMP) algorithm is used. The experimental comparison shows that the classification K-Singular Value Decomposition dictionary learning algorithm runs faster, and the Peak signal-to-noise Ratio (PSNR) and Structural Similarity (SSIM) value of the reconstructed image are higher.