Joint regularization based video super-resolution reconstruction method

Abstract

The invention discloses a joint regularization based video super-resolution reconstruction method, which comprises the steps of building a low-resolution observation model; building a universal video reconstruction cost function through a regularization based least mean square method; constructing a compensation based TV (CTV) regularization item, and assigning an adaptive weighting coefficient to a regional space so as to reduce adverse effects brought about by a registration error; constructing a multi-frame nonlocal low-rank (MNLR) regularization item; building a joint regularization based video super-resolution reconstruction cost function; solving the cost function by using a Split-Bregman iteration method so as to reconstruct high-resolution video. Video frames reconstructed according to the video super-resolution reconstruction method have abundant edge information, and hardly have any sawtooth effects. It is observed from the video reconstruction frames that the method disclosed by the invention is also excellent in ability of noise suppression, and the method has a very high reference value in an objective evaluation parameter. Therefore, disclosed by the invention is an effective video super-resolution reconstruction method.

Description

technical field [0001] The present invention designs a video super-resolution reconstruction method based on joint regularization. In particular, this regularization term not only considers the correlation within the video frame, but also takes into account the correlation between video frames, and can maintain the edge details of the video frame. Information at the same time, very good suppression of noise, belongs to the field of digital image processing. Background technique [0002] In the process of acquiring video, due to atmospheric disturbance, undersampling, system noise, optical and motion blur and other factors, the quality of the obtained video is degraded and the resolution is reduced. As people have higher and higher requirements on the definition of video, how to improve the resolution of video has become an urgent problem to be solved. Super-resolution reconstruction technology has attracted extensive attention from researchers at home and abroad because it ...

AI Technical Summary

Problems solved by technology

The Tikhonov regularization term is simple to implement, but this regularization blurs the edges of the image while constraining the noise. This method will make the reconstructed image too smooth

Regularization constraints based on total variation (TV) and bilateral total variation (BTV) are more commonly used image prior models, because they can remove noise while reconstructing a sharp image, preserving image edge details, But when the video is affected by large noise, the processing effect is not good in the flat area, it is easy to produce false edges, and it is easy to produce aliasing effect in the edge area

Images