Parameter Analysis on Backprojection for Super-Resolution (SR) Reconstruction Algorithm

Abstract

In this paper, an image SR reconstruction scheme by using k-Singular Value Decomposition (k-SVD) with Orthogonal Matching Pursuit (OMP) as sparse coding method was proposed to obtain the High-Resolution (HR) image. The system conducted in this paper consists of two parts: image SR reconstruction algorithm and also the backprojection process. Since this paper is focused on analysing the effect of parameters in backprojection on the performance of final images produced at the end of the process, therefore, the flow of backprojection is discussed. Generally, the backprojection algorithm is added to the approach to improve the image quality by sharpening the edges of the HR image. However, the parameters used in backprojection could be the factor that caused decrease in the image quality. Thus, the main idea of this paper is to analyse these parameters throughout the flow of backprojection algorithm. The parameters include the predefined 2D filter and interpolation method. Based on the results, it can be concluded that the use of averaging filter (hsize = 3), Gaussian filter (hsize = 5, 7 and 9; σ = 1) and bicubic interpolation method or also known as the cubic kernel can be adopted to the backprojection algorithm since these parameters achieved the highest RMSE, PSNR and SSIM values of 13.87, 25.29dB and 0.83 respectively. The analyses done in this paper has brought a clear understanding on the backprojection algorithm and further implementation and analysis in backprojection such as by using cascaded filters for averaging and Gaussian filters can be considered in the future.