A multi-target image restoration method

Abstract

The embodiment of the invention provides a multi-target image restoration method, which comprises the following steps of: constructing a parameterized wavelet basis function; Introducing the wavelet basis function into a strip wave function to form an interpolation multi-scale strip wave function; Forming a parameterized interpolation strip wave learning dictionary for different texture structuresof the image; Replacing the BV (Omega) space in the total variation model with the (shown in the description) of the BV space, introducing a parameterized interpolation strip wave learning dictionaryto form an image strip wave sparse representation variation model; Based on the image stripe wave sparse representation variation model, performing homotopy analysis on the multi-target image, and accurately segmenting the target image; And based on a homotopy segmentation result of the image, establishing a target-controllable image segmentation and restoration coupling variation model, and restoring the image. According to the method, an image target controllable segmentation and restoration coupling model is constructed based on interpolation Bendels (strip waves) sparse representation, and accurate restoration of a damaged target object in an image is realized.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, in particular to a method for repairing a multi-target image. Background technique [0002] Sequential microsection image analysis of insects plays an important role in revealing the reproduction of insects and the mechanism of action of pesticides on insects, which is helpful to the research and development of biological pesticides, reduces chemical pesticide pollution, and improves food safety. [0003] At present, compared with other cross-sectional image acquisition equipment, such as images obtained by nuclear magnetic resonance and laser confocal, microsection images have the advantages of thin slice thickness and clear images. However, the preparation of microsection images is affected by tools, microscopes, materials, and the preparation method itself, and the slice sequence images have the following problems. [0004] First, non-repeatability. Whether usi...

AI Technical Summary

Problems solved by technology

Different insect bodies are not identical to each other, so any artifacts present in the image cannot be picked up by repeated slices

[0005] Second, there will be image pollution problems such as wrinkles and knife marks in the continuous sectioning process

Due to the existence of wrinkles, information will be lost, which will inevitably affect the analysis and application of images

For image restoration, the recognition of discontinuous texture is the key to restoration, and the existing algorithms are usually unable to distinguish trivial texture and noise, which cannot meet the needs of image restoration and analysis

[0006] Third, it is impossible to accurately repair multi-target images

Therefore, none of the existing image inpainting models and segmentation models can achieve accurate inpainting and segmentation of damaged regions.

Images