Infrared Image Enhancement Using Differential Evolution Based on double plateau histogram equalization

2021 ◽  
pp. 757-770
Author(s):  
Dibakar Singh ◽  
Sushil Kumar
Keyword(s):  
Differential Evolution ◽  
Image Enhancement ◽  
Infrared Image ◽  
Histogram Equalization

Adaptive clip-limit-based bi-histogram equalization algorithm for infrared image enhancement

2020 ◽  
Vol 59 (28) ◽  
pp. 9032
Author(s):  
Abhisek Paul ◽  
Tandra Sutradhar ◽  
Paritosh Bhattacharya ◽  
Santi P Maity
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Equalization Algorithm

An Embedded Infrared Image Enhancement System Based on DSP and FPGA

2012 ◽  
Vol 505 ◽  
pp. 263-266
Author(s):  
Dong Mei Liu ◽  
Tao Zhang ◽  
Chuan Li Yin ◽  
Xiao Qiang Ji
Keyword(s):  
Real Time ◽  
Image Enhancement ◽  
High Speed ◽  
High Performance ◽  
Infrared Image ◽  
Processing System ◽  
Histogram Equalization ◽  
Running Time ◽  
Equalization Algorithm ◽  
Enhancement Algorithm

According to the disadvantage of the large noises of histogram equalization algorithm, a new adaptive image enhancement algorithm is presented. First, the statistical histogram of the infrared image is done. Then the threshold of plateaus Equalization is calculated and the statistical histogram is modified. Finally the bright values of the pixels of the image are changed. An embedded high speed image enhancement processing system on high performance DSP TMS320DM642 and FPGA was designed. Experimental results with real images shown that the system can improve the contrast of the infrared image, limit the noises of the enhancement image, and effectively enhance the infrared image, the running time of the program is shorter, so it can meet the requirements of real-time in the project.

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.

Adaptive Infrared Image Enhancement by Combining Differential Evolution with Stationary Wavelet Transformation

2011 ◽  
Vol 58-60 ◽  
pp. 2273-2278
Author(s):  
Chang Jiang Zhang ◽  
Yan Zhou
Keyword(s):  
Differential Evolution ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Evaluation Function ◽  
Low Contrast ◽  
Multi Scale ◽  
De Algorithm ◽  
Adaptive Enhancement ◽  
Robust To Noise ◽  
Enhancement Algorithm

Considering noise and low contrast of infrared image，an efficient nonlinear adaptive enhancement algorithm，which is based on differential evolution (DE)algorithm and stationary wavelet transform (SWT)，is proposed. Evaluation function is constructed by combing information entropy，signal-noise-ratio with standard deviation of enhanced image. A nonlinear transformation function is designed to enhance the contrast of the infrared image. The optimal transformation parameters are determined by combing DE algorithm with the constructed evaluation function. The proposed algorithm can efficiently enhance the contrast of the infrared image while have a good robust to noise. Experimental results show that the proposed algorithm is better than multi-scale nonlinear enhancement algorithm，stationary wavelet nonlinear enhancement algorithm and histogram equalization algorithm in overall performance.

scholarly journals PERBAIKAN CITRA INFRA MERAH DENGAN METODE CELLULAR AUTOMATA

2018 ◽  
Vol 18 (2) ◽  
pp. 61
Author(s):  
Annisa Yuniar Hidayah ◽  
Abduh Riski ◽  
Ahmad Kamsyakawuni
Keyword(s):  
Cellular Automata ◽  
Image Enhancement ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Initial State ◽  
Low Contrast ◽  
Linear Index ◽  
Infrared Cameras ◽  
Image Detail

Image enhancement is needed because not all images have good quality, such as noise, too low contrast or blurry image. These problems are commonly found in images generated from infrared cameras, therefore this study uses infrared imagery as an image to be corrected. The method that will be used to improve the image, namely Cellular Automata method. The edge detection using the Prewitt operator will be used as the initial state of Cellular Automata cells. The results obtained from this research is Cellular Automata method can improve the quality of infrared image well. Visually, the Cellular Automata method successfully improves image contrast and retains the infrared image detail so as not to reduce the value of information from the image. Calculated using the Linear Index of Fuzziness, the results of the Cellular Automata method are better only on some imagery only when compared to the Histogram Equalization mode. Keywords: Infrared Image, Image Enhancement, Cellular Automata

Sign in / Sign up

Export Citation Format

Share Document