An adaptive fusion of infrared and visible image based on learning of sparse fuzzy cognitive maps on compressive sensing

Abstract

In the recent trends of image processing, simultaneous image compression and fusion has become the major subject of interest. The advantage of image compression is that, it reduces the storage space and also reduces the bandwidth required for transmission. To get a high detailed compressed image, the fusion of visible and infrared images of the same scene is required. Infrared images provide hidden targets of the scene whereas visible images provide texture details of the scene. Hence if these two images are fused then it is possible to obtain a highly detailed image with all the hidden targets of the scene. This paper presents a novel method of visible and infrared image fusion based on compressed sensing and fuzzy logic. The visible and infrared images are converted with the Daubechies wavelet to decompose into lower and higher sub bands. The lower sub band is fused using maximum fusion rule, whereas the higher sub-bands are fused using effective type 2 fuzzy logic-based fusion rule. OOMP is used for retrieval of fused measured values into recovered sub-bands. The recovered higher and lower sub bands are reconstructed employing inverse discrete wavelet transform. The recommended technique is evaluated with several parameters such as Peak Signal Noise Ratio, Mean Square Error, Entropy, Mutual information, Standard deviation, Time and Compression Ratio. The results are compared to the existing techniques which show that the proposed method has a low computational complexity which produces a high detailed fused image with well-preserved edges.