Super-resolution Reconstruction of Noisy Video Image Based on Sparse Representation Algorithm
In this paper, the image super-resolution reconstruction (SRR) based on sparse representation was studied. Firstly, the sparse representation algorithm was simply analyzed, and then applied to the SRR processing of single image. In noisy video images, the Lucy-Rechardson algorithm was used for denoising first, then Lucas Kanade + multi-scale autoconvolution (MSA) method was used to register video images, and finally SRR was processed by sparse representation algorithm. Three video images were taken as examples for analysis, and the peak signal to noise ratio (PSNR) value and the structural similarity index measurement (SSIM) value were used as image quality evaluation indexes. The results showed that the average PSNR value and average SSIM of the SRR processing method based on sparse representation were significantly higher than those of bicubic interpolation method; the quality of the processed image was higher and the super-resolution effect was better. The experimental results prove the reliability of the proposed method and make some contributions to the further application of the sparse representation algorithm in SRR processing.
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