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

An Adaptive Low-Illumination Image Enhancement Method for Reducing Color Cast

An image enhancement and low illumination technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve problems such as over-suppression or over-enhancement of bright areas, poor processing of brighter areas of the image, and aggravated color cast of the image.

Active Publication Date: 2019-10-08
HARBIN INST OF TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The present invention aims to solve the problems of the existing low-illuminance image enhancement methods, such as the aggravation of the color shift of the image after enhancement, poor processing of the brighter areas of the image, resulting in over-suppression or over-enhancement of the brighter areas, and the existing method for different brightness images The problem of not being able to adaptively enhance 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
  • An Adaptive Low-Illumination Image Enhancement Method for Reducing Color Cast
  • An Adaptive Low-Illumination Image Enhancement Method for Reducing Color Cast
  • An Adaptive Low-Illumination Image Enhancement Method for Reducing Color Cast

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0075] Specific implementation mode one: combine figure 1 To describe this embodiment,

[0076] An adaptive low-light image enhancement method for reducing color shift, comprising the following steps:

[0077] Step 1, input the low-illuminance image L, and convert it to the RGB color space, and obtain the RGB three-channel image of the low-illuminance image L;

[0078] Step 2, performing an inverse S-type transformation on the RGB three-channel image of the low-illuminance image L, weakening the color shift phenomenon of the low-illuminance image, and obtaining the inverse image I; figure 2 is the inverse S-shaped transformation curve adopted;

[0079] Step 3: Invert the inverse image I to obtain the inverse image H, calculate the minimum value of each pixel of the inverse image H in the three channels of RGB, obtain the initial dark channel image D, and process the initial dark channel image value filtering to obtain the median filtering image D median , using the median...

specific Embodiment approach 2

[0089] The formula of the inverse S-type transformation described in step 2 of the present embodiment is as follows:

[0090] I(i,j)=255*(a-ln(-1+260 / (L(i,j)+4))) / b

[0091] Among them, I(i,j) and L(i,j) are the pixel points in row i and column j of the inverse image I and low-illumination image L respectively; a and b are transformation parameters, and the value of a is 4. The value of the transformation parameter b is 8.

[0092] Other steps and parameters are the same as those in the first embodiment.

specific Embodiment approach 3

[0094] The specific process of step 3 of this embodiment includes the following steps:

[0095] Step 31, using the formula H(i,j)=255-I(i,j) to invert the inverse image I to obtain an inverse image H;

[0096] Wherein, I(i,j) is row i in reverse image I, pixel point j; L(i,j) is row i in reverse image H, pixel point j column;

[0097] Step 32, use the formula Obtain the initial dark channel image D;

[0098] Among them, D(i, j) is the i-th row and j-th column pixel in the initial dark channel image D; min represents the minimum value operation; c is R, G, B, corresponding to the three red, green and blue in the RGB color space color channel, H c (i, j) is the i-th row and the j-th column pixel point of a certain channel of the inverted image H in the RGB color space;

[0099] Step 33: Perform a median filter operation on the initial dark channel image to obtain a median filter image. The specific calculation formula is as follows:

[0100]

[0101] Among them, D medi...

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 self-adaptive low-light level image intensification method for reducing color cast, relates to low-light level image intensification methods, and aims to solve the problems that the image color cast is intensified when a conventional low-light level image intensification method is used, and a relatively bright area of an image is over-inhibited or over-intensified when being not well processed. The self-adaptive low-light level image intensification method comprises the following steps: firstly, converting a low-light level image into a RGB (Red, Green, Blue) color space, performing inverted S-shaped conversion, performing inversion, calculating minimum values of different pixel points of reversed images at three RGB channels so as to obtain initial dark channel images, and performing median filtering so as to obtain atmosphere light intensity estimation values; converting the inversion images into an HSV color space, and calculating self-adaptive intensification parameters by taking average gray level values of a V channel as average brightness; calculating transmissivity images according to atmosphere imaging equations, modifying so as to obtain transmissivity smooth images, with the atmosphere imaging equations, performing demisting operation on the three RGB channels of the inversion images, performing inversion, and performing S-shaped conversion, thereby obtaining finally intensified images. The self-adaptive low-light level image intensification method is applicable to intensification processing on images.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a low-illuminance image enhancement method. Background technique [0002] With the development of optical system manufacturing technology and optical detection technology, various optical imaging devices are increasingly used in civilian and military fields, such as civilian optical digital photography and camera systems, intelligent optical video surveillance systems and military Optical imaging guidance system and optical imaging reconnaissance system. However, when the detector sensitivity is relatively low and the ambient light conditions are insufficient, the images formed by these optical systems have various degradation problems, such as reduced image contrast, insufficient brightness, etc., resulting in the inability of human eyes or digital image processing systems to clearly and accurately distinguish Target and background, it is difficult to obtain the target i...

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 Patents(China)
IPC IPC(8): G06T5/00G06T7/90
CPCG06T2207/10024G06T5/92G06T5/73
Inventor 遆晓光张雨
Owner HARBIN INST 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