Super resolution reconstruction based on total variation regularization

Super resolution reconstruction is an important branch of image processing that extracting high resolution images containing more details from an image sequence of low resolution, by image processing such as motion estimation, de-blurring and de-noising. Currently super resolution is an economical and practical algorithm that can be used to improve image resolution in remote monitoring, remote sensing and medical imaging. In this thesis, in order to obtain high resolution image from an image sequence of low resolution and improve the image quality, visual effects, total variation algorithm is used to estimate the motion of low resolution images caused by the restriction of environmental conditions and the physical limitations of imaging equipment. This algorithm contains a lot of processing technologies, such as, motion estimate, motion compensation, image fusion, de-nosing. Experiment result shows that the entropy of the high resolution image was improved and the D and D_index are improved with the increasing of frames, so clearly high resolution image can be obtained from source image by using this algorithm. The super resolution algorithm mentioned in the thesis with high practical application value, can be applied to long-range remote sensing and face image restoration.