We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble （SBHE） sam- piing for compressive sensing imaging. First, source images are compressed by compressive sensing, to facili...We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble （SBHE） sam- piing for compressive sensing imaging. First, source images are compressed by compressive sensing, to facilitate the transmission of the sensor. In the fusion phase, the image gradient is calculated to reflect the abundance of its contour information. By com- positing the gradient of each image, gradient-based weights are obtained, with which compressive sensing coefficients are achieved. Finally, inverse transformation is applied to the coefficients derived from fusion, and the fused image is obtained. Information entropy （IE）, Xydeas＇s and Piella＇s metrics are applied as non-reference objective metrics to evaluate the fusion quality in line with different fusion schemes. In addition, different image fusion application scenarios are applied to explore the scenario adaptability of the proposed scheme. Simulation results demonstrate that the gradient-based scheme has the best per- formance, in terms of both subjective judgment and objective metrics. Furthermore, the gradient-based fusion scheme proposed in this paper can be applied in different fusion scenarios.展开更多
基金supported by the National S&T Major Program(No.9140A1550212 JW01047)the ‘Twelfth Five’ Preliminary Research Project of PLA(No.402040202)
文摘We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble （SBHE） sam- piing for compressive sensing imaging. First, source images are compressed by compressive sensing, to facilitate the transmission of the sensor. In the fusion phase, the image gradient is calculated to reflect the abundance of its contour information. By com- positing the gradient of each image, gradient-based weights are obtained, with which compressive sensing coefficients are achieved. Finally, inverse transformation is applied to the coefficients derived from fusion, and the fused image is obtained. Information entropy （IE）, Xydeas＇s and Piella＇s metrics are applied as non-reference objective metrics to evaluate the fusion quality in line with different fusion schemes. In addition, different image fusion application scenarios are applied to explore the scenario adaptability of the proposed scheme. Simulation results demonstrate that the gradient-based scheme has the best per- formance, in terms of both subjective judgment and objective metrics. Furthermore, the gradient-based fusion scheme proposed in this paper can be applied in different fusion scenarios.
