An Automated System to Accelerate Image Reconstruction Using GPU
Shivani V. Gaikar, Sandip M. Walunj · 2016
Whenever an image is distorted numbers of algorithms are used to reduce the distortions in the image. Couple of times algorithms are used to delete the distorted objects from the digital images. One of the algorithms used to do this ROI (Region-of-interest) approach. In this approach the salient object is reconstructed but background image remains blur also these approaches are more time consuming. To overcome this issue, we propose method called HEMS (Hierarchical Exemplar-Based Matching Synthesis) using GPU, in which once salient object regions are encoded only quantized color feature and local descriptor of background are kept achieving bit rate reduction. So to reconstruct the background image there are 3 steps: Firstly, search image from database, which have relevant images, limitating search to feasible number of patches. Secondly, patches are matched by color features to select appropriate candidates. Finally, distorted optimized image synthesis makes it possible to automatically choose most suitable texture sample and reconstruct the image. At last the algorithm is given to GPU which automatically reduces the time for reconstructing the images.