the effectiveness of our method and demonstrate its superiority to other robust super-resolution methods.

A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion erros, inaccurate blur models, noise, moving objects, motion blur etc. This tobusiness is needed since super-resolution methods are very sensitive

show the effectiveness of our method and demonstrate its robustness to other super resolution methods.

This paper presents a hybrid approach for images and video super-resolution. We have proposed the approach for enhancing the resolution of images and low resolution, under sampled videos. We exploited the shift and motion based robust super-resolution (SR)algorithm [1] and the diffusion image

Abstract. 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 (i.e. regularization) is often

Abstract. 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

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Abstract: A new approach for efficient and robust super-resolution is presented in this paper. The essence of the proposed method is in selectively applying a Bayesian MAP estimator to visually important image regions. Regions with high spatial activity are considered as visually important

In the last two decades, many papers have been published, proposing a variety of methods for multi- frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses