Optimization Methods for Medical Image Super Resolution Reconstruction
Super resolution (SR) concentrates on constructing a high resolution (HR) image of a scene from two or more sets of low resolution (LR) images of the same scene. It is the process of combining a sequence of low resolution (LR) noisy blurred images to produce a higher resolution image. The reconstruction of high resolution images is computationally expensive. SR is defined to be an inverse problem that is well known as ill conditioned. The SR problem has been reformulated using optimization techniques to define a solution that is a close approximation of the true scene and less sensitive to errors in the observed images. This paper reviews the optimized SR reconstruction approaches and highlights its challenges and limitations. An experiment has been done to compare between bicubic, iterative back projection (IBP) projected onto convex sets (POCS) total variation (TV) and Gradient descent via sparse representation. The experimental results show that Gradient descent via sparse representation outperforms other optimization techniques.
Applications of Intelligent Optimization in Biology and Medicine