|
|
|
| Infrared image enhancement method based on regional saliency recognition |
| 1. School of Electronic Information, Nanjing Vocational College of Information Technology, Nanjing, Jiangsu 210023, China; 2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China; 3. People′s Liberation Army of China 94826, Shanghai 200020, China |
|
|
|
|
Abstract To solve the problems of insufficient texture details and contrast reduction in infrared images, the infrared image enhancement method was proposed based on the regional saliency recognition. The saliency and non-saliency regions of infrared images were recognized based on the constructed infrared image saliency feature map. The transmission map of the inverted infrared image was estimated via the dark channel prior, and the estimated transmission map was corrected based on the recognition results. The enhanced infrared image was obtained based on the simplified atmospheric scattering model, and the corresponding edge future was further enhanced via the change of details prior. The experiments were conducted using various types of infrared images, and the proposed method was subjectively and objectively compared with several current mainstream infrared image enhancement methods. The experimental results show that the proposed method has good robustness, and the average new visible edge ratio and the average contrast gain can reach 4.15 and 6.47, respectively. The human-vision-based image visibility can be improved by 33％.
|
|
Received: 19 January 2019
|
|
|
|
| ［1］ |
WU W, YANG X M, LI H, et al. A novel scheme for infrared image enhancement by using weighted least squares filter and fuzzy plateau histogram equalization［J］. Multimedia Tools and Applications, 2017,76(23): 24789-24817.
|
| ［2］ |
LI Y, ZHANG Y F, GENG A H, et al. Infrared image enhancement based on atmospheric scattering model and histogram equalization［J］. Optics ＆ Laser Technology, 2016, 83: 99-107.
|
| ［3］ |
温海滨, 毕笃彦, 马时平,等. 消除阶梯效应与增强细节的变分Retinex红外图像增强算法［J］. 光学学报, doi:10.3788／AOS201636.0911005.
|
|
WEN H B, BI D Y, MA S P, et al. Variational retinex algorithm for infrared image enhancement with staircase effect suppression and detail enhancement［J］. Acta Optica Sinica, doi:10.3788／AOS201636.0911005. (in Chinese)
|
| ［4］ |
GU Z F, CHEN C, ZHANG D Y. A low-light image enhancement method based on image degradation model and pure pixel ratio prior［J］. Mathematical Problems in Engineering, doi: 10.1155／2018／8178109.
|
| ［5］ |
袁小燕,张照锋,张登银,等.基于融合技术的单幅红外图像增强方法［J］.电子器件,2018,41(4):976-985.
|
|
YUAN X Y, ZHANG Z F, ZHANG D Y, et al. A fusion-based single infrared image enhancement method［J］. Chinese Journal of Electron Devices,2018,41(4):976-985. (in Chinese)
|
| ［6］ |
GU Z F, JU M Y, ZHANG D Y. A single image deha-zing method using average saturation prior［J］. Mathematical Problems in Engineering,doi: 10.1155／2017／6851301.
|
| ［7］ |
JU M Y, ZHANG D Y, WANG X M. Single image dehazing via an improved atmospheric scattering model［J］. Visual Computer, 2017, 33(12):1613-1625.
|
| ［8］ |
LI J F, ZHANG H, YUAN D, et al. Single image dehazing using the change of detail prior［J］. Neurocomputing, doi: 10.1016／j.neucom.2015.01.026.
|
| ［9］ |
STARK J A. Adaptive image contrast enhancement using generalizations of histogram equalization［J］. IEEE Transactions on Image Processing, 2000, 9(5): 889-896.
|
| ［10］ |
GU Z F, JU M Y, ZHANG D Y. A novel Retinex image enhancement approach via brightness channel prior and change of detail prior［J］. Pattern Recognition ＆ Image Analysis, 2017, 27(2):234-242.
|
| ［11］ |
CHOI L K, YOU J, BOVIK A C. Referenceless prediction of perceptual fog density and perceptual image defogging［J］. IEEE Transactions on Image Processing, 2015, 24(11): 3888-3901.
|
|
|
|
2019, Vol. 40 
