Images store a lot of information and are the window for human beings to understand things. A lot of research is devoted to analyzing and processing images, which is called image processing in a broad sense. Image processing includes image recognition, image restoration, image enhancement, image coding and so on. This paper mainly focuses on the field of image restoration. Image restoration, also known as image inverse problem, aims to restore high-quality original images from degraded or damaged observations. It also acts as a preprocessing step in many intermediate and advanced image processing tasks. Due to the limitations of sensors or environmental conditions, imaging systems usually have factors such as noise, optical or motion blur, resulting in image degradation and distortion. Aiming at the ill posed problem of image pixel missing and blur in the process of compression coding, this paper uses GMM model to solve the degraded image, so as to achieve the purpose of image restoration.
Multi video super-resolution algorithms reconstruct high spatio-temporal resolution video by exploiting complementary information in multiple low-resolution video sequences. Aiming at improving spatio-temporal resolution of video for real-world applications, an algorithm is proposed using Maximum Posterior Likelihood - Markov Random Field (MAP-MRF) and implemented on camera array. Compared with the current algorithms for super-resolution reconstruction, the suggested algorithm is advantageous in keeping the edge sharpness and detailed texture, and robust against the noises. The experimental result has confirmed the effectiveness of the proposed method under the practical conditions such as large displacement and motion aliasing.
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