Gradient-based compressive image fusion

基于梯度的压缩感知图像融合（英文）

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.

目的:面向多传感器图像融合,实现基于梯度的压缩感知图像融合,使其具有传输量小,计算复杂度低的特点。创新点:提出一种基于梯度的融合规则(图1),对压缩感知系数进行融合,并对融合后的压缩感知系数进行反变换得到原图像,提高压缩感知融合质量。方法:首先,对多传感器捕获的图像进行压缩感知分解以提高传感器传输速率。然后在融合阶段,基于压缩感知系数梯度进行融合得到融合后的压缩感知系数,并对融合后的系数进行压缩感知反变换得到融合后图像。通过两种融合场景的应用实验(图2-7,表1-6),证明所提算法相比于其他传统压缩感知图像融合方法,在人眼视觉及客观融合标准中均更优。结论:针对多种融合场景,提出一种高效的基于梯度的压缩感知的图像融合方法,提高图像融合精度。