Image Super-resolution

Super-resolution is the task of obtaining a high-resolution image of a scene given low resolution image(s) of the scene. Applications of super-resolution include forensic, satellite, medical imaging, surveillance, displaying video on large screens, etc [1]. Obtaining high-resolution images directly via better hardware (better image sensors, larger chip size) is quite costly. Furthermore, most of the smart-phones today would be hard-pressed to incorporate hardware enhancements for achieving high resolution images. Hence, a signal processing approach to increase the resolution of images obtained from the phone camera is desirable.

[1]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[2]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[3]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[4]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Sunil Arya,et al.  Approximate nearest neighbor queries in fixed dimensions , 1993, SODA '93.

[6]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..