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

Improved method for enhancing picture contrast based on histogram

A technology of image comparison and histogram, applied in image enhancement, image data processing, instruments, etc., can solve the problems of compressed grayscale and high probability of overstretching.

Inactive Publication Date: 2009-09-09
SICHUAN PANOVASIC TECH
View PDF0 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to provide an improved histogram-based image contrast enhancement method to overcome the grayscale with high probability of overstretching and the grayscale with low probability of compression in the prior art when the global histogram equalization method enhances image contrast. Grayscale flaws

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
  • Improved method for enhancing picture contrast based on histogram
  • Improved method for enhancing picture contrast based on histogram
  • Improved method for enhancing picture contrast based on histogram

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0023] In this embodiment, we adaptively set the value of gamma between [0, 1] for the attributes of the original image.

[0024] First, divide the grayscale of the image into N grayscales, and accumulate the number of pixels of each grayscale stsN[i], where i is the serial number of the grayscale;

[0025] Then, according to the distribution of the histogram, the minimum number n of continuous gray scales accounting for the ratio R of image pixels and the central position p of continuous gray scales are calculated. Such as figure 1 As shown, the length of the rectangular frame is n, the ratio of the number of image pixels in the frame to the entire image pixel is R, and the abscissa of the center of the rectangular frame is p. From figure 1 , we can see that the minimum number of continuous gray levels n reflects the concentration of the image gray level distribution. Calculate the gamma value based on the minimum number n of continuous gray levels and the central position...

Embodiment 2

[0035] In Embodiment 1, the correlation between adjacent images of the video sequence is not considered, so there will be bright and dark dithering. In this embodiment, the similarity of the video image histogram of the same scene can be simply used for scene discrimination, and the adjacent video frames of the same scene can be constrained to prevent light and dark jitter.

[0036] Since adjacent video images of the same scene have a certain correlation in the distribution of luminance components, the absolute value of the difference between the number of each gray level and the ratio sign to the number of pixels in the entire image should be less than a certain value, Generally, it is 20% to 50%, and the optimum is 40%. In this embodiment, sign=40%, the absolute value of the difference between the number of each gray level and the ratio sign of the number of pixels in the entire image is less than 40%, that is, the same scene image.

[0037] If the current image is not the ...

Embodiment 3

[0043] In Embodiment 1, when the fading in and fading out of the scene is not considered, the gray scale of the image is only distributed in a part of the gray scales. At this time, even if the gamma value is small, excessive stretching will occur. For this reason, in this embodiment, a smaller value is added to the number of pixels stsN[i] of each gray scale, and then gamma transformation is performed to obtain the converted number of gray pixels of each gray scale for traditional histogram equalization processing to obtain a contrast-enhanced output image.

[0044] Divide the grayscale of the image into 64 grayscales, that is, when N=64, we set the smaller value to 0.1, then this step is expressed by the formula,

[0045] sts_gamma64[i]=(sts64[i]+0.1) gamma (3)

[0046] In the formula, sts64_gamma64[i] represents the number of grayscale pixels of each level after gamma transformation when the grayscale of the image is divided into 64 grayscales.

[0047...

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 improved method for enhancing picture contrast based on histogram, comprising the following steps of: dividing picture gray into a plurality of gray levels and accumulating the pixel dimensions of each gray level; valuing for picture gray (gamma) in the range of (0, 1); gamma converting the pixel dimensions of each gray level by gamma value to obtain the converted pixel dimensions of each gray level; and treating the pixel dimensions of each gray level which is obtained by gamma converting in a way of traditional histogram equalization to obtain output picture with enhanced contrast. When the gamma is valued as (0, 1), gamma function conversion reduces larger value in histogram probability distribution so as to enlarge lower value without causing the larger value to be less than the lower value. Therefore, the excessive stretching of large probability gray level and the excessive compression of small probability gray level can be reduced, meanwhile, the contrast is improved. Namely, the degree of the histogram equalization can be adjusted by adjusting gamma value.

Description

technical field [0001] The invention relates to an improved image contrast enhancement method based on histogram in post-processing. Background technique [0002] The traditional global domain histogram equalization method is based on the principle of maximum entropy, set p r (r) is the probability density function of each gray level of the original image. The histogram equalization process is actually to find a gray level transformation function T, so that the changed gray level value s=T(r), so that each gray level The probability density function p of s (s) are equal, then the image has the largest information entropy, thus enhancing the contrast of the image. The basic steps are as follows: [0003] (1) Statistical histogram of the original image: [0004] P r ( r k ) = n k N ...

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): G06T5/40
Inventor 杨东陈涛刘强
Owner SICHUAN PANOVASIC 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