Abstract
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.
Highlights
The higher the resolution of an image, the clearer the image and the stronger the ability to express details
Taking peak signal to noise ratio (PSNR) and structural similarity index measurement (SSIM) as image quality evaluation indexes, the method in this paper was compared with the bicubic interpolation method, and the results showed that the PSNR value and SSIM value of the method in this paper are both higher
The results showed that the image obtained by super-resolution reconstruction (SRR) processing under the sparse representation designed in this paper had better super-resolution effect, which proves the reliability of this method
Summary
The higher the resolution of an image, the clearer the image and the stronger the ability to express details. After a certain imaging process for highresolution (HR) scenes, low-resolution (LR) images are obtained due to degradation processes such as blurring and noise, but LR images are required in many applications. The commonly used methods to improve image quality include image denoising, restoration, enhancement, and image super-resolution reconstruction (SRR). SRR refers to a method of reconstructing an HR image through one or more LR images [1]. SRR technology is an ill-posed inverse problem [2, 3], which can acquire LR images without changing the hardware conditions, and it is of great value in the field of image processing. Sparse representation algorithms are widely used in SRR processing [4]
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