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 in-
crease of the reconstruction quality. In terms of the reconstruction al-
gorithms, 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 selec-
tive extrapolation. Besides PSNR gains of +0.31 dB to +0.68 dB
relative to a random quarter sampling mask resulting from our opti-
mized mask, visually noticeable enhancements are perceptible.