L1 regularized super-resolution from unregistered omnidirectional images

In this paper, we address the problem of super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We propose an l1 regularized total least squares norm minimization method for joint registration and reconstruction with better stabilization and denoising. Experimental results show that regularization offers a quality improvement of up to 1dB. In addition, it reduces the number of low resolution images that are necessary to reconstruct a high resolution image at a target quality.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Zafer Arican, Pascal Frossard
• Keywords: ICASSP 2009, Low-resolution Omnidirectional Images, Omnidirectional Vision Scenarios, Resolution Image, Signal Processing