低秩矩阵恢复下的自适应图像超分辨率重建OACSTPCD

The pixel density of low resolution adaptive images is too sparse,which results in image sharpness not meeting super-resolution standards.Therefore,an adaptive image super-resolution reconstruction method based on low rank matrix restoration is proposed.A low rank matrix restoration model is constructed,the peak signal-to-noise ratio(PSNR)parameters are calculated,and the adaptive image denoising processing based on low rank matrix restoration is completed.Adaptive features in the image region are extracted,and the specific reconstruction function expressions are defined based on super-resolution discrimination conditions,and then the design of adaptive image super-resolution reconstruction methods for low rank matrix recovery is completed.The experimental results show that the application of the proposed method can effectively denoise the image,with an SNR ratio higher than 31 dB and a mean image resolution of 100 PPI after reconstruction.Therefore,this method can achieve super-resolution reconstruction.