Robust super-resolution by minimizing a Gaussian-weightedL2error norm
Tuan Q. Pham, Lucas J. van Vliet, Klamer Schutte · Journal of Physics Conference Series · 2008
Super-resolution restoration is the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. Additional constraints such as smoothness of the solution via regularization is often required to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L 2 -norm in the data misfit term that does not response to intensity outliers. With the outliers suppressed, our solution behaves similarly to a maximum-likelihood solution in the presence of Gaussian noise. The effectiveness of our algorithm is demonstrated with super-resolution restoration of real infrared image sequences under severe aliasing and intensity outliers.