  Quarter sampling is a novel sensor concept that enables the acquisition of
higher resolution images without increasing the number of pixels. This is
achieved by covering three quarters of each pixel of a low-resolution sensor
such that only one quadrant of the sensor area of each pixel is sensitive to
light. By randomly masking different parts, effectively a non-regular sampling
of a higher resolution image is performed. 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 enhanced using super resolution
algorithms. Recently, algorithms based on machine learning such as the Very
Deep Super Resolution network (VDSR) proofed to be successful for this task. In
this work, we transfer the concepts of VDSR to the special case of quarter
sampling. Besides adapting the network layout to take advantage of the case of
quarter sampling, we introduce a novel data augmentation technique enabled by
quarter sampling. Altogether, using the quarter sampling sensor, the image
quality in terms of PSNR can be increased by +0.67 dB for the Urban 100 dataset
compared to using a low-resolution sensor with VDSR.
