Design Techniques for Incremental Non-Regular Image Sampling Patterns

TL;DR

This paper introduces two novel techniques for designing incremental non-regular image sampling patterns that improve reconstruction quality by reducing aliasing and optimizing sampling density.

Contribution

The paper presents two new methods for creating optimized non-regular sampling patterns that incrementally add pixels, enhancing image reconstruction quality.

Findings

Patterns increase PSNR by over 0.5 dB

Improved sampling reduces aliasing effects

Visual quality is enhanced compared to traditional patterns

Abstract

Even though image signals are typically acquired on a regular two dimensional grid, there exist many scenarios where non-regular sampling is possible. Non-regular sampling can remove aliasing. In terms of the non-regular sampling patterns, there is a high degree of freedom in how to actually arrange the sampling positions. In literature, random patterns show higher reconstruction quality compared to regular patterns due to reduced aliasing effects. On the downside, random patterns feature large void areas which is also disadvantageous. In the scope of this work, we present two techniques to design optimized non-regular image sampling patterns for arbitrary sampling densities. Both techniques create incremental sampling patterns, i.e., one pixel position is added in each step until the desired sampling density is reached. Our proposed patterns increase the reconstruction quality by more than +0.5 dB in PSNR for a broad density range. Visual comparisons are provided.