Simultaneous edge preserving and noise mitigating image super-resolution algorithm

State-of-the-art single image super-resolution (SISR) methods provide faithful reconstruction, but involve a training step using large database, demanding high computations. We propose a method which reduces the execution time significantly by eliminating the training process. To preserve the edges, stationary wavelet transform (SWT) is employed. Further image enhancement and noise sensitivity depletion is achieved using complex diffusion based shock filter by operating in the dual dominant mode. These filtered subbands are combined to generate a high resolution (HR) image. Further artifacts are removed by projecting onto a global image vector space iteratively. Experimental results show that the performance of the proposed method is superior to the existing methods.