Access Restriction

Author Baboulaz, Loïc ♦ Dragotti, Pier Luigi
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Input Image ♦ Step Edge ♦ Standard Local Feature Detection Approach ♦ Spline Function ♦ Sampling Kernel ♦ Super-resolved Image ♦ Index Term Image Super-resolution ♦ Image Super-resolution ♦ Subpixel Extraction ♦ Low Resolution Multiview Image ♦ Local Feature ♦ Image Superresolution Algorithm ♦ Consecutive Step ♦ Local Feature Extraction ♦ New Technique ♦ Synthetic Image ♦ Subpixel Harris Corner Detector ♦ Registration Technique ♦ Recent Result ♦ Image Restoration ♦ Real Low Resolution Image ♦ Visual Quality ♦ Image Edge Analysis ♦ Full Frame Super-resolution ♦ Finite Rate ♦ Much Attention ♦ Image Registration ♦ Low Resolution Image ♦ Present Result
Description IEEE Int. Conf. on Image Processing
The problem of image super-resolution from a set of low resolution multiview images has recently received much attention and can be decomposed, at least conceptually, into two consecutive steps as: registration and restoration. The ability to accurately register the input images is key to the success and the quality of image superresolution algorithms. Using recent results from the sampling theory for signals with Finite Rate of Innovation (FRI), we propose in this paper a new technique for subpixel extraction from low resolution images of local features like step edges and corners for image registration. By exploiting the knowledge of the sampling kernel, we are able to locate exactly the step edges on synthetic images. We also present results of full frame super-resolution of real low resolution images using our registration technique. We obtain super-resolved images with a much improved visual quality compared to using a standard local feature detection approach like a subpixel Harris corner detector. Index Terms — Image super-resolution, Image edge analysis, Image registration, Spline functions, Image restoration.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2007-01-01