  Quarter sampling and three-quarter sampling are novel sensor concepts that
enable the acquisition of higher resolution images without increasing the
number of pixels. This is achieved by non-regularly covering parts of each
pixel of a low-resolution sensor such that only one quadrant or three quadrants
of the sensor area of each pixel is sensitive to light. Combining a properly
designed mask and a high-quality reconstruction algorithm, a higher image
quality can be achieved than using a low-resolution sensor and subsequent
upsampling. For the latter case, the image quality can be further enhanced
using super resolution algorithms such as the very deep super resolution
network (VDSR). In this paper, we propose a novel end-to-end neural network to
reconstruct high resolution images from non-regularly sampled sensor data. The
network is a concatenation of a locally fully connected reconstruction network
(LFCR) and a standard VDSR network. Altogether, using a three-quarter sampling
sensor with our novel neural network layout, the image quality in terms of PSNR
for the Urban100 dataset can be increased by 2.96 dB compared to the
state-of-the-art approach. Compared to a low-resolution sensor with VDSR, a
gain of 1.11 dB is achieved.
