Compressed Image Regularized Spatio-temporally Adaptive Super-resolution Reconstruction
压缩图像空时自适应正则化超分辨率重建

Super-resolution(SR) technique is the task of estimating High-resolution(HR) images from a sequence of Low-resolution(LR) observations,which has been a great focus for compressed images.Based on the theory of regularization,a novel spatio-temporally adaptive SR algorithm is developed and analyzed using the information from the compressed bitstream.A new form of regularized cost function to control the balance between temporal data and spatial prior information is proposed.An iterative gradient descent algorithm is utilized to reconstruct the HR image.The regularization parameter is simultaneously estimated at each iteration step in the reconstruction process of the HR image.Experimental results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality,and it is applicable for compressed images.