  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.
