Heterogeneous parallel implementation of single image super resolution using transformed self-exemplars on multicore & TitanX GPU

Abstract

Super-resolution (SR) can be defined as a method of reconstructing high resolution(HR) image by increasing spatial resolution of one or more low resolution(LR) image counterparts. SR is widely applied in areas like satellite imaging, drones, surveillance and medical imaging etc. SR techniques can be broadly classified as external database driven or internal database driven approaches. The first approach is time consuming during training phase, in learning LR-HR patch relationship but computes fast during testing phase. The second approach directly constructs the super resolved image from existing LR image without the need of any training procedures but found to be computationally intensive during test time. This paper focuses on reducing the computation time of transformed self exemplar based internal database driven technique [1] that uses 3D scene geometry and internal image characteristics for super resolution by exploiting its highly parallelizable nature. On parallelizing the original algorithm we achieved a speedup of 6 on multi-core, upto 10 on GPU, around 14 on hybrid platform of multi-core and GPU. Using our hybrid implementation we can achieve 32x super resolution factor. We compare our results with SRCNN[2] (Super Resolution Convolution Neural Network) an external database SR technique which has a very good performance among the current start-of-the-art SR methods.