Super-resolution with randomly shaped pixels and sparse regularization

This paper shows a random and distinct shape of each pixel improves the performance of super-resolution using multiple input images. Since the spatial light sensitivity distribution in each pixel of an image sensor is rectangular and identical, the process of imaging is equivalent to the point sampling of blurred image which is a result of convolution of a rectangle with the original image. The convolution results in a loss of the high spatial frequency component of the original image, which limits the performance of super-resolution. Thus, we sprayed a fine-grained black powder on an image sensor to give a random code to the spatial light sensitivity distribution in each pixel. This approach was combined with a reconstruction technique based on sparse regularization, which is commonly used in compressed sensing, in an experiment with an actual setup. A high-resolution image was reconstructed from a limited number of input images and the performance of super-resolution was significantly improved.