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Image sharpness lifting method based on sparse expression

A technology of image clarity and sparse representation, which is applied in the field of image processing to achieve the effect of enriching detailed information, clear details, and improving image clarity

Active Publication Date: 2017-10-24
云南联合视觉科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] So far, there is no technology that has the functions of image fusion, high-resolution image reconstruction and image denoising at the same time, so that the final fusion image effect retains the rich details of the source image

Method used

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  • Image sharpness lifting method based on sparse expression
  • Image sharpness lifting method based on sparse expression
  • Image sharpness lifting method based on sparse expression

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Experimental program
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Embodiment 1

[0027] Embodiment 1: A method for improving image clarity based on sparse representation, the specific steps of the method are as follows:

[0028] Step1, the input two pieces of CT and MRI image that pixel size is 256*256 (as image 3 (a), 3(b)), perform low-rank decomposition respectively, and obtain sparse partial images and low-rank partial images respectively; (that is, obtain a low-rank partial image A after CT image decomposition 1 and a sparse partial image A 2 , the MRI image is decomposed to obtain a low-rank partial image B 1 and a sparse partial image B 2 );

[0029] Step2, use the dictionary learning model to select the image set Y (such as Figure 8 As shown, using a high-resolution non-medical image set, this embodiment selects 6 pictures to construct an image set) for training, and obtains a low-rank dictionary D L and a sparse dictionary D S ; The dictionary learning model is:

[0030]

[0031] s.t.||Z S || 0 ≤T 0 ,||Z L || 0 ≤T 1

[0032] whe...

Embodiment 2

[0036] Embodiment 2: a method for improving image clarity based on sparse representation, the specific steps of the method are as follows:

[0037] Step1, input two noisy CT and MRI images with a pixel size of 256×256 (such as image 3 (c), 3(d)), perform low-rank decomposition respectively, and obtain sparse partial images and low-rank partial images respectively; (that is, after CT image decomposition, a low-rank partial image A 1 and a sparse partial image A 2 , the MRI image is decomposed to obtain a low-rank partial image B 1 and a sparse partial image B 2 );

[0038] Step2, use the dictionary learning model to select the image set Y (such as Figure 8 As shown, using a high-resolution non-medical image set, this embodiment selects 6 pictures to construct) for training, and obtains a low-rank dictionary D L and a sparse dictionary D S ; The dictionary learning model is:

[0039]

[0040] s.t.||Z S || 0 ≤T 0 ,||Z L || 0 ≤T 1

[0041] where Y is denoted as ...

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Abstract

The invention discloses an image sharpness lifting method based on sparse expression; the method comprises the following steps: inputting two source images and respectively decomposing with low rank; using a dictionary learning model to train a selected image set; using a sparse expression method to carry out sparse fusion for the low rank part image and sparse part image, respectively using a quadrature coupling tracking algorithm to solve the low rank fusion image and the sparse fusion image, thus obtaining sparse coefficients corresponding to the two part images; linearly combining the low rank dictionary with the solved sparse coefficient, thus obtaining a combined image; then using the sparse expression method to make sparse reconstruction for the combined image, thus obtaining a reconstructed image; using the quadrature coupling tracking algorithm to solve the reconstructed image so as to obtain the sparse coefficient; carrying out sparse expression for the obtained sparse coefficient with two dictionaries so as to obtain the fusion image. When viewed from the subjective visual sense or objective evaluation indexes, the experiment result and fusion effect of the image sharpness lifting method are much better with other conventional methods.

Description

technical field [0001] The invention relates to a method for improving image clarity based on sparse representation, which belongs to the field of image processing. Background technique [0002] In the field of image processing, image high-resolution reconstruction technology is a promising research. In recent years, image high-resolution reconstruction technology has attracted more and more researchers' attention. Many researchers have proposed many High-resolution image reconstruction technology method. The so-called high-resolution image reconstruction technology uses a group of low-quality, low-resolution images (or motion sequences) to generate a single high-quality, high-resolution image. The application field of high-resolution image reconstruction is extremely broad, and it has important application prospects in military, medical, public security, computer vision, etc. At present, there are two main categories of high-resolution techniques: reconstruction-based met...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06T5/00G06T5/50
CPCG06T5/50G06T2207/20221G06V10/40G06V10/513G06T5/70
Inventor 李华锋邓志华余正涛王红斌
Owner 云南联合视觉科技有限公司