Colour image enhancement with brightness preservation and edge sharpening using a heat conduction matrix

被引:11
作者
Katircioglu, Ferzan [1 ]
机构
[1] Duzce Univ, Duzce Vocat Sch, Dept Elect & Automat, Duzce, Turkey
关键词
image colour analysis; image enhancement; heat conduction; brightness; image resolution; matrix algebra; colour image enhancement; brightness preservation; edge sharpening; heat conduction matrix; enhancement process; heat conduction equation; solid fluids; stagnant fluids; colour images; colour channel; RGB colour image; HSI colour model; feature matrix; negative values; positive values; zero values; negative HCM value; level enhancement; positive HCM value; level values; mean brightness value; RGB colour model; colour image details; ADAPTIVE GAMMA CORRECTION; HISTOGRAM EQUALIZATION; QUALITY ASSESSMENT; CONTRAST;
D O I
10.1049/iet-ipr.2020.0393
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM) can have negative, positive or zero values. A pixel with a small negative HCM value indicates that I needs level enhancement for a good image, whereas a small positive HCM value means that the I level value will be reduced and aligned with its neighbours. High positive or negative values are defined as the edges of the objects and the I level values of such pixels are not changed to protect the edges. In addition, whether HCM is negative or positive, the balanced increment and decrement path at a level I ensures that the mean brightness value performs natural protection. Finally, an enhanced image is obtained by transitioning from the HSI to the RGB colour model. Experimental results show that this method can enhance colour image details better than other methods.
引用
收藏
页码:3202 / 3214
页数:13
相关论文
共 35 条
[1]  
[Anonymous], 2015, International Journal of Image Processing (IJIP)
[2]  
Bychkovsky V, 2011, PROC CVPR IEEE, P97
[3]   Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs [J].
Chen, Yu-Sheng ;
Wang, Yu-Ching ;
Kao, Man-Hsin ;
Chuang, Yung-Yu .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :6306-6314
[4]   Model-Assisted Multiband Fusion for Single Image Enhancement and Applications to Robot Vision [J].
Cho, Younggun ;
Jeong, Jinyong ;
Kim, Ayoung .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2018, 3 (04) :2822-2829
[5]   A generalized gamma correction algorithm based on the SLIP model [J].
Deng, Guang .
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2016,
[6]  
Devi A.S., ARXIV20101009, P1478
[7]  
Gonzalez R.C., 2006, PEARSON ED INDIA, Vthird
[8]   Measuring colourfulness in natural images [J].
Hasler, D ;
Süsstrunk, S .
HUMAN VISION AND ELECTRONIC IMAGING VIII, 2003, 5007 :87-95
[9]   Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution [J].
Huang, Shih-Chia ;
Cheng, Fan-Chieh ;
Chiu, Yi-Sheng .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (03) :1032-1041
[10]   Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images [J].
Huang, Zhenghua ;
Zhang, Tianxu ;
Li, Qian ;
Fang, Hao .
INFRARED PHYSICS & TECHNOLOGY, 2016, 79 :205-215