Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Nonlocal median value blind noise reduction method based on visual perception

A visual perception, non-local technology, applied in the field of image processing, to achieve the effects of suppressing noise, protecting detailed information, and improving restoration accuracy

Active Publication Date: 2016-06-15
ANQING NORMAL UNIV
View PDF2 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the defects or deficiencies in the prior art, the present invention aims to propose a non-local median blind denoising method based on visual perception, which can effectively suppress the impulsive noise model, noise density and image complexity. Noise, and completely protect the details of the image

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
  • Nonlocal median value blind noise reduction method based on visual perception
  • Nonlocal median value blind noise reduction method based on visual perception
  • Nonlocal median value blind noise reduction method based on visual perception

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.

[0039] Aspects of the invention are described in this disclosure with reference to the accompanying drawings, which show a number of illustrated embodiments. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of numerous ways, since the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.

[0040] According to the embodiment of the present invention, in general, the non-local median blind noise reduction method b...

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 provides a nonlocal median value blind noise reduction method based on visual perception, comprising the following steps: constructing an impulse noise blind detector based on the visual outlier measure of the pixels in a digital image, wherein the visual outlier measure is obtained by quantizing the visual similarities in impulse noise of different models and fusing different visual feature quantization results; extracting the nonlocal information of the image, and constructing a nonlocal median value calculation model; calculating regularization parameters according to the visual outlier measure and the nonlocal information, and establishing a nonlocal median value regularization item; and building a nonlocal median value noise reduction functional model, and adaptively repairing the noise pixels in the image. According to the blind noise reduction method of the invention, impulse noise of different models and densities in the digital image is processed in a unified manner according to the visual features of impulse noise, image self-similarity and outlier data mining, and the problem that noise pixels are hard to repair effectively due to unknown impulse model, high-density noise and image multi-modal complexity in an actual noise reduction process is solved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to digital image noise reduction technology, in particular to a non-local median blind noise reduction method based on visual perception, which is suitable for blind noise reduction of unknown model impulse noise in digital images. Background technique [0002] Impulse noise is a common type of interference signal in digital images, which is caused by imperfections and errors in imaging systems, transmission media, and recording equipment during image acquisition, transmission, and storage. Impulse noise is usually divided into three types according to the distribution of brightness values, which are fixed value, random value, and mixed type of fixed value and random value. Suppressing impulse noise in digital images is the premise and foundation of image analysis, understanding and recognition, and it is also an important and difficult issue in this field. For the...

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
CPCG06T2207/20032G06T5/70
Inventor 朱柱江巨浪胡积宝占生宝
Owner ANQING NORMAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com