Tensor Completion With DCT Based Gradient Method

Abstract

Tensor Completion from a limited number of non-distorted observations, has enticed researchers interest. The color image has been considered as the three dimensional tensor. Low rank property in Optimization has been used to recover the tensors in the image. The Low rank prior alone not enough to tensor completion. The traditional tensor truncated nuclear norm approaches have been able to approximate the real rank of the tensor, but these are low rank prior approaches. Here a transformation-based optimization method has been proposed to complete the tensors of the image. The Discrete Cosine Transformation (DCT) has been used as transformation method. The tensor singular value decomposition (t-SVD) and accelerated proximal gradient line (APGL) approaches have been considered. The Full Reference metrics i.e., peak signal to noise ratio (PSNR) and structural similarity (SSIM) have been used to evaluate the proposed approach. The obtained results are superior to the existing algorithms. The PSNR and SSIM have been recorded as 27.30 dB and 0.8845 respectively.