Abstarct: Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Image super-resolution (SR) reconstruction is the process of generating an image at a higher spatial resolution by using one or more low-resolution (LR) inputs from a scene. The early works on superresolution (often designed for grayscale images), although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. On another front, single frxame demosaicing methods developed to reduce color artifacts, often fail to completely remove such errors. In this thesis, we use the statistical signal processing approach to propose an effective frxamework for fusing low-quality images and producing higher quality ones. In our proposed method, the objective functional is formed by an adaptive strategy depending on the accuracies of the estimated low resolution image observation models. This strategy serves to adaptively weight low-resolution images according to their reliability and can add robustness in practical implementation of super-resolution. Also, extending this method to RGB field, we proposed an adaptive robust hybrid method of super-resolution and demosaicing, which increases the spatial resolution and reduces the color artifacts of a set of low-quality color images. Experimental results on synthetic and real data sets confirm the effectiveness of our methods.