Image processing method and device based on Shannon-Blackman wavelet sparse representation

Abstract

The invention discloses an image processing method and device based on Shannon-Blackman wavelet sparse representation. The method comprises steps as follows: a to-be-processed image is acquired; a multi-scale interpolation operator based on Shannon-Blackman interpolating wavelets is established, and the image is amplified; an image variation model for the amplified image under a wavelet frame is established; and the image variation model under the wavelet frame is solved to obtain a sharp image. According to the Shannon-Blackman wavelet sparse representation based image processing method and device, the image is amplified through the multi-scale interpolation operator, the image variation model under the wavelet frame is established, the image is solved through a sparse grid algorithm to obtain a super-resolution image, and the image processing efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method and device based on Shannon-Blackman wavelet sparse expression. Background technique [0002] High-resolution biomedical images can provide more accurate and rich visual information for medical diagnosis and pathological analysis. Although the use of high-density image acquisition sensors can improve the image resolution to a certain extent, due to the limitation of the sensor arrangement density of the imaging system itself, the cost is high and the noise pollution in the image acquisition process cannot be completely avoided; increasing the chip size will lead to charge transfer A decrease in speed and an increase in capacitance. Therefore, using super-resolution image reconstruction technology to improve image resolution has important scientific significance and practical value. [0003] Variational models for image processing have been sh...

AI Technical Summary

Problems solved by technology

However, the commonly used difference method and linear interpolation method inevitably appear artificial artifacts and distortions during image super-resolution reconstruction; The diffusion function does not have multi-scale characteristics, resulting in unclear local detail structure of the enlarged image, which affects the improvement of image resolution

In order to solve this problem, the partial differential equation denoising model has experienced the development process from low-order to high-order, vector diffusion to tensor diffusion, real diffusion to complex diffusion, but this method will correct the enlarged image. The local small boundary is blurred, and its image processing effect has not been obvious

[0004] In addition, when the traditional difference method solves the parabolic nonlinear partial differential equation, the stability of the algorithm is high, and the calculation accuracy and efficiency are not high, which affects the application and promotion of the algorithm.

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