Single image super-resolution reconstruction method based on enhanced non-local total variational model a priori

Abstract

The invention discloses a single image super-resolution reconstruction method based on an enhanced non-local total variational model a priori. The method mainly comprises the following steps of: carrying out bicubic interpolation on an input low-resolution image to obtain an initial high-resolution estimation; the non-local similar pixel group corresponding to each pixel is obtained by using the multi-offset search strategy. Based on the attenuation kernel strategy, the weights of the offset target image blocks attenuated with the offset distance are assigned in the multi-offset search process. Based on the stable group similarity reliability strategy, the similarity reliability between each group of similar pixels and the target pixel is obtained. The super-resolution cost function basedon enhanced non-local total variational is constructed and the high-resolution image is solved. Repeat these steps until the number of iterations reaches a preset value. The image reconstructed by theinvention has obvious advantages in both subjective and objective effects, so the invention is an effective single image super-resolution reconstruction method, and can be widely used in the fields of military, medical, agriculture and the like.

Description

Technical field [0001] The invention relates to a non-local variational model and image super-resolution reconstruction technology, in particular to an enhanced non-local total variational model prior, and applying the prior to single-image super-resolution reconstruction, belonging to digital image processing The direction of image restoration in the field. Background technique [0002] With the vigorous development of computer science and information science, digital visual signals such as images / videos have become more and more widely used in the fields of military, medical, agriculture, and people’s livelihoods. This also makes great contributions to high-resolution images / videos. Application requirements. However, due to the limitations of the capture equipment and shooting environment, the final captured images / videos will inevitably have a certain degree of degradation (such as insufficient resolution, noise pollution, blurring, etc.), which may cause the quality of the c...

AI Technical Summary

Problems solved by technology

However, there are still two major problems in this model: (1) In the multi-offset processing, the influence of the distance into the offset is not considered for target blocks with different offsets; (2) The traditional unnormalized weights are used to define non-normal Local flow, resulting in the reliability of non-locally similar pixels cannot be accurately estimated

These problems will affect the ability of AHNLTV to perform non-local modeling on the image (especially in the high-frequency area of ​​the image), and then affect the restoration of the high-frequency information of the image.

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