Iterative Optimization of Quarter Sampling Masks for Non-Regular Sampling Sensors

TL;DR

This paper introduces an iterative algorithm to optimize quarter sampling masks for non-regular sampling sensors, significantly improving image reconstruction quality across various algorithms.

Contribution

It proposes a novel iterative method to enhance quarter sampling masks, leading to better reconstruction quality in non-regular sampling sensors.

Findings

PSNR gains of +0.31 dB to +0.68 dB over random masks

Noticeable visual improvements in reconstructed images

Effective across multiple reconstruction algorithms

Abstract

Non-regular sampling can reduce aliasing at the expense of noise. Recently, it has been shown that non-regular sampling can be carried out using a conventional regular imaging sensor when the surface of its individual pixels is partially covered. This technique is called quarter sampling (also 1/4 sampling), since only one quarter of each pixel is sensitive to light. For this purpose, the choice of a proper sampling mask is crucial to achieve a high reconstruction quality. In the scope of this work, we present an iterative algorithm to improve an arbitrary quarter sampling mask which results in a continuous increase of the reconstruction quality. In terms of the reconstruction algorithms, we test two simple algorithms, namely, linear interpolation and nearest neighbor interpolation, as well as two more sophisticated algorithms, namely, steering kernel regression and frequency selective extrapolation. Besides PSNR gains of +0.31 dB to +0.68 dB relative to a random quarter sampling mask resulting from our optimized mask, visually noticeable enhancements are perceptible.