Wavelet Domain Superresolution reconstruction of Infrared Image Sequences

摘要

Many automatic target recognition, detection, and identification problems usually suffer from lack of adequate resolution of the image data, especially among infrared imaging systems. A number of superresolution reconstruction algorithms have been proposed. The challenge is how to recapture additional high-frequency information from adjacent frames in an image sequence that contains slightly different, but unique, information. In addition, real-world infrared sequence images are noisy with low contrast, and low spatial resolution. Since broad-banded noise mainly affects high-frequency information to be recaptured, the challenge is how to avoid smoothing out the high-frequency data by the regularization are not smoothed out. This paper presents a new superresolution reconstruction approach based on wavelet domain for superresolution image reconstruction of infra-red (IR) sequences. Minimizing the regularization cost function in wavelet domain forms a multi-scale high-resolution estimate. The effects of noise are incorporated into the iterative process in the proposed method. The estimation errors in high- and low-frequency bands are processed separately to solve the problem of variable correlations of observed images and slow convergence. The proposed approach was tested on the infrared aerial image sequences provided by Defence Research Establishment in Valcartier. Experiment results show that a significant increase in the spatial resolution can be achieved by the proposed approach while the noise is smoothed out.