An improved image super resolution and its parallel implementation based on CUDA

Image super resolution (SR) based on self-similarity has achieved impressive results by exploiting the local similarity across various scaled images. However, it is time consuming and far from meeting practical applications´ requirements and it is prone to unnatural visual artifacts such as facets and enhanced noise. In this paper, with the powerful general purposeGPU, an accelerated parallel implementation based on NVIDIA CUDA architecture has been proposed. Then denoising is combined and implemented together during the SR processing using the patch matching information of similarity, and it makes the images clearer and cleaner. Also, subpixel accuracy of interpolation and searching is applied to improve the similarity matching process, which make the upscaled images more natural-looking. Experimental results demonstrate that the proposed method achieves an impressive speedup rate and produces improved visual quality images in comparison with other state-of-the-art SR.