Visual sensation plays a remarkable role in shaping our picture of the world. A vast proportion of information gained from the reality becomes a part of our mind through our sight. Consequently imaging techniques have been becoming inevitable related to the storing and presentation of information as well. The resolution of digital imaging devices has been increasing rapidly. However, even more detailed information is needed in several places, such as the wide research field of medical imaging and machine vision.
The development of imaging technology and sensors has led to the increase in the resolution of digital pictures. Nowadays computational capacity has been increasing fast too, so post-processing techniques have been becoming more and more common. According to English terminology this group of algorithm is called computational photography. This term means that images become more accurate thanks to post-processing techniques.
The methods of superresolution also belong to the family of computational photography algorithms. These aim at creating an accurate image representation via several low-resolution captures of the reality. While doing my research, I tried to discover this active research field.
With the devices enabling high parallelism such as FPGA (Field-Programmable Gate Array) computational capacity has improved. The aim of my thesis was to investigate the implementation of the superresolution algorithm FPGA.