scholarly journals Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 248 ◽  
Author(s):  
Chaoxuan Qin ◽  
Xiaohui Gu
Keyword(s):  
Weight Function ◽  
Image Enhancement ◽  
Fitness Function ◽  
Infrared Image ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Histogram Equalization ◽  
Inertia Weight ◽  
Enhancement Method ◽  
Improved Pso

In this paper, an improved PSO (Particle Swarm Optimization) algorithm is proposed and applied to the infrared image enhancement. The contrast of infrared image is enhanced while the image details are preserved. A new exponential center symmetry inertia weight function is constructed and the local optimal solution jumping mechanism is introduced to make the algorithm consider both global search and local search. A new image enhancement method is proposed based on the advantages of bi-histogram equalization algorithm and dual-domain image decomposition algorithm. The fitness function is constructed by using five kinds of image quality evaluation factors, and the parameters are optimized by the proposed PSO algorithm, so that the parameters are determined to enhance the image. Experiments showed that the proposed PSO algorithm has good performance, and the proposed image enhancement method can not only improve the contrast of the image, but also preserve the details of the image, which has a good visual effect.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals New Combined Technique for Fingerprint Image Enhancement

2016 ◽  
Vol 11 (1) ◽  
pp. 222 ◽  
Author(s):  
Alaa Ahmed Abbood ◽  
Mohammed Sabbih Hamoud Al-Tamimi ◽  
Sabine U. Peters ◽  
Ghazali Sulong
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Fingerprint Image ◽  
Pixel Intensity ◽  
Improve Image Quality ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Fingerprint Image Enhancement ◽  
Normalization Process ◽  
Combined Technique

This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one pixel-wide lines. Finally, the Fusion technique was used to merge the results of the Histogram Equalization process with the Skeletonization process to obtain the new high contrast images. The proposed method was tested in different quality images from National Institute of Standard and Technology (NIST) special database 14. The experimental results are very encouraging and the current enhancement method appeared to be effective by improving different quality images.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals Image Enhancement of Cross-Border E-Commerce Logistics Video Surveillance Based on Partial Differential Equations

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaosheng Yu ◽  
Peili Wu
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Fractional Derivative ◽  
Image Enhancement ◽  
Video Surveillance ◽  
Empirical Formula ◽  
Histogram Equalization ◽  
Partial Differential ◽  
Cross Border ◽  
Enhancement Method

Although the development time of cross-border e-commerce in China is very short, the scale of its transactions and the speed of development are amazing, and as a supporting foundation for promoting economic and trade globalization, cross-border e-commerce has an extremely important strategy for this guiding role. This not only brings new opportunities to cross-border e-commerce companies but also excavates a huge potential market for the logistics industry. Cross-border e-commerce not only breaks through the trade barriers between countries; it makes trade move towards borderlessness and at the same time triggers major changes in international trade. This paper introduces partial differential equations into the video surveillance image enhancement system of cross-border e-commerce logistics. Aiming at the shortcomings of the contrast enhancement method based on gradient field equalization, this paper proposes a partial differential enhancement method based on histogram equalization. By proposing a gradient transformation function, the edges and textures with relatively small gradient values are enhanced to make the original weaker texture details clearer. In order to better adjust the brightness and contrast of the image, combined with histogram equalization, we propose an inverse equalization transformation. When the histogram equalization and the inverse equalization transform are combined reasonably, the brightness and contrast of the image can be adjusted very well. In this paper, the finite difference method is used for discretization when solving partial differential equations, and Euler’s equation is obtained by applying the principle of least squares. By introducing the heat equation, the direct solution of Euler’s equation is converted into an iterative form, which greatly reduces the amount of calculation. This article uses statistical methods to obtain the empirical formula of the fractional differential order. This empirical formula makes the calculation of the order of the fractional derivative easy and can be extended to other fractional image enhancement models and overcomes the shortcomings of the traditional fractional derivative order obtained through experience or a large number of experiments. Experiments show that the proposed algorithm not only enhances detailed texture information but also improves image clarity, overall brightness, and contrast without color distortion. The objective evaluation indicators also show the superiority of the algorithm.

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

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