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

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.

5 pages, 8 figures, 25th IEEE International Conference on Image Processing (ICIP)

References in corpus (2)

• Resampling Images to a Regular Grid from a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction
• Reducing Randomness of Non-Regular Sampling Masks for Image Reconstruction

Cited by in corpus (4)

• Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation
• Design Techniques for Incremental Non-Regular Image Sampling Patterns
• Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network
• Novel Consistency Check For Fast Recursive Reconstruction Of Non-Regularly Sampled Video Data