A Novel Interpolation Based Super-Resolution Of The Cropped Scene From A Video

Super resolution (SR) image reconstruction is the process of combing several low resolution images into a single high resolution image. The videos of the image change frame to frame. This paper is based on interpolation super-resolution method. An algorithm for enhancing the resolution of the scene through Segmentation of the video and cropping the required part of the scene, super-resolution using Interpolation, Regression, and Post-processing, is applied to the effective Super-resolution image output. Further object tracking and identification use the results of this work. We worked in traffic surveillance videos.

[1]  Wu Bo,et al.  Application of Adaptive Kernel Matching Pursuit to Estimate Mixture Pixel Proportion , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[2]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[3]  Deqing Sun,et al.  Postprocessing of Low Bit-Rate Block DCT Coded Images Based on a Fields of Experts Prior , 2007, IEEE Transactions on Image Processing.

[4]  Kwang In Kim,et al.  Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Mei Han,et al.  Bilateral Back-Projection for Single Image Super Resolution , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[6]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.