Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Super-resolution reconstruction is a method for reconstructing higher resolution images from a set of low resolution observations. The sub-pixel differences among different observations of the same scene allow to create higher resolution images with better quality. In the last thirty years, many methods for creating high resolution images have been proposed. However, hardware implementations of such methods are limited. In this work, highly parallel and pipelined implementation for iterative back projection super-resolution algorithm is presented. The proposed hardware implementation is capable of reconstructing 512×512 sized images from set of 20 lower resolution observations, with real-time capabilities up to 25 frame per second (fps). Explained system has been synthesized and verified via Xilinx VC707 FPGAs. To the best of our knowledge, the system is currently the fastest super-resolution implementation based on FPGA.
,