Parallel super-resolution reconstruction based on neighbor embedding technique

Abstract

Super Resolution (SR) is a technique to recover a high-resolution (HR) image from different noisy low resolution (LR) images. The missing highfrequency components in LR images should be restored correctly in HR image. Because of the extensive size of satellite images, the utilize to parallel algorithms can accomplish results more quickly with accurate results. This paper proposes an accelerated parallel implementation for an example based super-resolution algorithm, Neighbor Embedding (NE), using GPU. The NE trains the dictionary with patches obtained from a single image in the training phase. Euclidean distances are used to obtain the optimal weights that will be used in the construction of high-resolution images. Compute Device Unified Architecture (CUDA) by NVidia’s has been used to implement the proposed parallel NE. Different experiments have been carried out on a synthetic test image and satellite test image. The proposed GPU implementation of the NE was benchmarked against the serial implementation. The experimental results show that the speed of the implementation depends on the image size. The speed of the GPU implementation compared to the serial one using CPU ranged from 20× for small images to more than 30× for large image size.