Survey of Superresolution Using Phased Based Image Matching

Superresolution (SR) is a method of enhancing image resolution by combining information from multiple images. Two main processes in superresolution are registration and image reconstruction. Both of these processes greatly affect the quality image of the superresolution. Accurate registration is required to obtain high-resolution image quality. This research propose a collaboration between Phase-Based Image Matching (PBIM) registration, and reconstruction using Structure Adaptive Normalized convolution algorithm (SANC) and Projection Onto Convex sets algorithm (POCs). PBIM was used to estimate translational registration stage. We used the function fitting around the peak point, to obtain sub pixel accurate shift. The results of this registration were used for reconstruction. Three registration method and two reconstruction algorithms have been tested to obtain the most appropriate collaboration by measuring the value of Peak Signal to Noise Ratio (PSNR). The result showed that the collaboration of PBIM and both reconstruction algorithm, SR with PBIM and POCs have PSNR average of 32.12205, while PSNR average of SR with SANC algorithm was 32.07325. For every collaborative algorithms that have been tested, registration PBIM with function fitting, has an higher average PSNR value than the Keren and Marcel registration.