Curvelet redundant dictionary based immune optimization image reconstruction

Abstract

The invention discloses a curvelet redundant dictionary based immune optimization image reconstruction method, which solves the problem that the present 10-norm reconstruction technology acquires a reconstructed image with a poor visual effect, and is realized through the following steps: (1) clustering observation vectors; (2) initializing populations; (3) para-immunity optimizing; (4) reconstructing an initial image; (5) filtering and projecting onto convex sets; (6) judging whether or not the iteration number achieves the maximum value; (7) updating the sparsity; (8) updating the populations; (9) immune optimizing an image block; and (10) reconstructing the image. According to the invention, similar observation vectors are solved to obtain a common set of curvelet ground atoms by using the immune clone optimization technology, and then each observation vector is solved to obtain a set of curvelet ground atoms through combining the filtering and the projecting onto the convex sets. The curvelet redundant dictionary based immune optimization image reconstruction method eliminates the block effect in the reconstructed image, and obtains a reconstruction image with better visual effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an immune-optimized image reconstruction method based on a Curvelet redundant dictionary in the fields of image reconstruction, image acquisition equipment development and the like. The invention is used for obtaining high-quality and clear images during image reconstruction, and can reduce hardware cost when used in the field of image acquisition equipment development. Background technique [0002] In the field of compressed sensing image reconstruction, in order to solve the sparse representation coefficients of image signals under some kind of orthogonal base dictionary or some kind of transformation, the method from l 0 A method for solving sparsely represented coefficient approximations in non-convex optimization problems in the norm sense. At present, the commonly used method is the greedy pursuit algorithm, which approximates the original signal by selectin...

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Problems solved by technology

The disadvantage of this patent is that the parameter λ, which controls the balance between the reconstruction error and the signal sparsity, has a great influence on the entire solution process and the optimal solution. Inappropriate parameters will reduce the image decomposition speed and affect the image reconstruction effect.

[0005] To sum up, although the method based on matching pursuit has a relatively fast running time, it does not consider the inherent characteristics of the image signal itself. The selection of the parameter λ in the existing technology is not adaptive. To obtain the optimal solution, it is necessary to manually select It is very troublesome for the whole solution process, and if the value of the parameter is not selected properly, it is difficult to get the optimal solution

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