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

Low-light image enhancement method based on noise attention map guidance

A low-light image and noise technology, applied in the field of image processing, can solve problems such as low visibility and noise pollution of low-light images, and achieve the effects of good generalization ability, noise removal, and good noise removal performance.

Pending Publication Date: 2021-11-12
XIAN UNIV OF TECH
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a low-light image enhancement method based on the guidance of the noise attention map, which solves the problems of low-light image low visibility and serious noise pollution in the prior art

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
  • Low-light image enhancement method based on noise attention map guidance
  • Low-light image enhancement method based on noise attention map guidance
  • Low-light image enhancement method based on noise attention map guidance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0016] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0017] The present invention mainly proposes a low-light image enhancement method based on the noise attention map to guide the enhancement network. The overall idea is as follows: first, the original low-light image is input into the noise estimation module, and the noise estimation module uses the spatial attention mechanism to effectively learn the spatial position Weights to focus different degrees of noise in different regions of the original low-light image. The spatial noise position weight obtained by the noise estimation module and the original low-light image are used as the input of the enhancement network, and the spatial noise position weight obtained by the noise estimation module is used to guide the enhancement network to remove the noise in the image more effectively. Under the guidance of the spatial noise position weight ma...

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 a low-light image enhancement method based on noise attention map guidance, and the method comprises the steps: 1, constructing a noise estimation module which inputs an original low-light-level image, wherein the size of the original low-light-level image is H * W * 3, and the output of the noise estimation module is a characteristic pattern with the size of H * W * 1; and 2, constructing an enhanced network module, wherein the input data of the enhanced network module are the output features in the step 1 and the original low-light image, the size of the enhanced network module is H * W * 4, the output of the enhancement network module is an enhanced image, the size of the enhanced image is H * W * 3, and the enhanced network module comprises an encoder, a ResidualBlock and a decoder. The method provided by the invention can effectively enhance a single low-light image, has good denoising performance under the guidance of a noise attention graph, does not cause color distortion, can retain texture details of an original graph, and has good generalization ability on different data sets.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to RGB true color image restoration technology, and relates to a low-light image enhancement method guided by a noise attention map. Background technique [0002] Today, images can provide a large amount of dynamic information, and the transmission of information through images plays an increasingly important role in people's lives. Images acquired under low-light conditions have the "three low characteristics" of low contrast, low brightness, and low signal-to-noise ratio, which severely limits the recognition and interpretation of image content; it also affects subsequent image processing tasks, such as image segmentation, Target recognition and video surveillance, etc. Although the brightness of the image can be improved to a certain extent by prolonging the exposure time of the camera, it is easy to generate a lot of image noise during this period. Therefore, how to im...

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
IPC IPC(8): G06T5/00G06T7/90G06T9/00G06N3/04G06N3/08
CPCG06T9/002G06T7/90G06N3/04G06N3/08G06T2207/10024G06T2207/20081G06T2207/20084G06T5/70
Inventor 孙帮勇赵兴运
Owner XIAN UNIV OF TECH
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