Image Super-Resolution Reconstruction Based on Sparse Representation and POCS Method

Abstract

Abstract. The traditional projection onto convex set (POCS) algorithm can reconstruct a low resolution (LR) image, but it is contradictory in retaining image detail and denoising, so the quality of a reconstructed image is limited. To avoid defects of POCS and obtain higher resolution, the image denoising idea based on sparse representation is led into this paper. Sparse representation can learn well the optimized overcomplete sparse dictionary of an image, which has self-adaptive property to image data and can describe image essential features so as to implement the goal of denoising efficiently. At present, K-singular value decomposition (K-SVD) is the emerging image processing method of sparse representation and has been used widely in image denoising. Therefore, combined the advantages of K-SVD and POCS, a new image ISR method is explored here. In terms of signal noise ratio (SNR) values and the visual effect of reconstructed images, simulation results show that our method proposed has clear improvement in image resolution and can retain image detail well.

Research on super-resolution reconstruction of remote sensing images: a comprehensive review

“…In the convex set projection method based on reconstruction, the traditional projection on convex sets (POCS) algorithm has contradictions in preserving image details and denoising, which affects the quality of the reconstructed image. In order to avoid this defect and obtain higher resolution, Shang 26 introduced the idea of image denoising based on sparse representation on the basis of POCS, combined the image processing method of sparse representation of K-singular value decomposition with the advantages of POCS for image SR reconstruction. Patti et al 27,28 proposed another method of SR reconstruction of POCS, which took into account the blur factors caused by non-zero aperture time, camera movement, and imaging optical components.…”

Spatial Scale Effect of a Typical Polarized Remote Sensor on Detecting Ground Objects

“…Its inverse process is the super-resolution image reconstruction. In this paper, the super-resolution image reconstruction technology used is mainly based on the POCS algorithm [ 34 , 35 ].…”