Reducing Randomness of Non-Regular Sampling Masks for Image Reconstruction

TL;DR

This paper investigates how reducing the randomness in non-regular sampling masks improves image reconstruction quality and manufacturing efficiency, demonstrating that smaller-scale non-regularity suffices for high PSNR gains.

Contribution

It shows that less random, smaller-scale non-regular sampling masks can achieve comparable or better image reconstruction results, simplifying mask manufacturing.

Findings

Smaller-scale non-regular masks improve PSNR.

Reduced randomness simplifies manufacturing.

Efficient storage of sampling masks.

Abstract

Increasing spatial image resolution is an often required, yet challenging task in image acquisition. Recently, it has been shown that it is possible to obtain a high resolution image by covering a low resolution sensor with a non-regular sampling mask. Due to the masking, however, some pixel information in the resulting high resolution image is not available and has to be reconstructed by an efficient image reconstruction algorithm in order to get a fully reconstructed high resolution image. In this paper, the influence of different sampling masks with a reduced randomness of the non-regularity on the image reconstruction process is evaluated. Simulation results show that it is sufficient to use sampling masks that are non-regular only on a smaller scale. These sampling masks lead to a visually noticeable gain in PSNR compared to arbitrary chosen sampling masks which are non-regular over the whole image sensor size. At the same time, they simplify the manufacturing process and allow for efficient storage.