Supercharge Your Innovation With Domain-Expert AI Agents!

Image denoising method based on Treelet transformation and minimum mean-square error estimation

A minimum mean square error and image technology, applied in the field of image processing, can solve problems such as inaccurate similarity weights and high computational complexity

Inactive Publication Date: 2011-10-19
XIDIAN UNIV
View PDF6 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the similarity between image blocks is calculated directly using the grayscale in the noisy image, and the obtained similarity weight is inaccurate, which makes the final denoising result deviate from the original image. When the image block is rotated, the computational complexity is relatively high

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image denoising method based on Treelet transformation and minimum mean-square error estimation
  • Image denoising method based on Treelet transformation and minimum mean-square error estimation
  • Image denoising method based on Treelet transformation and minimum mean-square error estimation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] Attached below figure 1 The steps of the present invention are further described in detail.

[0054] Step 1, input an image to be denoised.

[0055] Step 2, search for similar image blocks.

[0056] 2a) Select the central image patch and search window.

[0057] For the input noisy image X, take any pixel as the center, take 5-11 pixels as the side length, and take a square area as the center image block. In the embodiment of the present invention, a central pixel block with a size of 7×7 is selected. With 21-41 pixels as the side length, determine a square search window. In the embodiment of the present invention, a search window with a size of 39×39 is selected.

[0058] 2b) Select image blocks. All image blocks of the same size as the central image block are selected by scanning line by line in the search window.

[0059] 2c) Select similar image blocks

[0060] Calculate the similarity between the central image block in step 2a) and the image block selected in ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an image denoising method based on Treelet transformation and minimum mean-square error estimation, mainly solving the problem that the existing denoising method is poor in denoising effect of the natural images corroded by Gaussian white noise. The image denoising method comprises the following steps of: (1) inputting an image to be denoised; (2) searching similar image blocks; (3) obtaining a basis matrix by Treelet transformation; (4) projection; (5) calculating the coefficient after denoising; (6) calculating a similar image block matrix after denoising; (7) calculating the similarity; (8) valuating the matrix; (9) judging whether all the image blocks in the image are processed completely, if so, carrying out a step (10), and if not, carrying out a step (12); and (10) outputting a result. The image denoising method has the advantage that the denoising effect of the natural images containing Gaussian white noise, can restore the original characteristics of the image, and can be used for image preprocessing, such as image segmentation, target recognition and transformation detection and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a denoising method of Treelet transform and minimum mean square error estimation in the technical field of natural image processing and filtering. This method can be used for digital image preprocessing in fields such as forest resource investigation, disaster assessment, urban planning, medical imaging, and astronomical imaging. Background technique [0002] Due to the limitations of various conditions, the image will be affected by various noises in the process of acquisition, encoding, and transmission, which will bring disadvantages to other subsequent processing tasks such as image segmentation and target recognition. Noise is necessary. Image denoising solves the problem of image quality degradation caused by noise interference, suppresses the influence of noise, improves image quality, and is the basis of image post-processing. [0003] At present, people ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 王桂婷焦李成张林刚公茂果钟桦张小华田小林侯彪王爽
Owner XIDIAN UNIV
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com