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Abstract: We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble (SBHE) sampling 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 compositing 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 performance, 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.

Within the SBHE (scrambled block Hadamard ensemble) sampling and GPSR (gradient projection for sparse reconstruction), authors analyzed six image fusion weighted schemes and proved that among them, gradient-based weighting provides the best results, in terms of subjective and objective judgements. Experiments are conducted under two typical image fusion scenarios: (1) thermal and visible image fusion and (2) multifocus image fusion.

基于梯度的压缩感知图像融合

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