Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images

TL;DR

This paper introduces a texture-dependent frequency selective reconstruction method for non-regularly sampled images, improving reconstruction quality by allocating iterations based on regional texture, leading to PSNR gains up to 1.47 dB.

Contribution

It proposes a novel texture-dependent approach that optimally distributes iterations in frequency selective reconstruction, enhancing image quality for non-regular sampling.

Findings

Achieves up to 1.47 dB PSNR improvement.

Reduces unnecessary iterations in homogeneous regions.

Enhances visual quality of reconstructed images.

Abstract

There exist many scenarios where pixel information is available only on a non-regular subset of pixel positions. For further processing, however, it is required to reconstruct such images on a regular grid. Besides many other algorithms, frequency selective reconstruction can be applied for this task. It performs a block-wise generation of a sparse signal model as an iterative superposition of Fourier basis functions and uses this model to replace missing or corrupted pixels in an image. In this paper, it is shown that it is not required to spend the same amount of iterations on both homogeneous and heterogeneous regions. Hence, a new texture-dependent approach for frequency selective reconstruction is introduced that distributes the number of iterations depending on the texture of the regions to be reconstructed. Compared to the original frequency selective reconstruction and depending on the number of iterations, visually noticeable gains in PSNR of up to 1.47 dB can be achieved.