Abstract Most traditional infrared and visible image fusion methods often ignore noise in acquisition or transmission and their performance inevitably decreases in practical applications. To address this problem, a new and effective variational model is proposed for simultaneous image fusion and denoising. In an l 2 − l p norm minimization setting with p = 0 and p = 1 respectively, the hybrid l2 norm fidelity term is built to preserve image intensity and details from both infrared and visible images. And the nonconvex l0 norm and convex l1 norm sparsity constraints are applied to reduce noise while preserving important image fine features. Furthermore, a computationally efficient numerical algorithm based on half-quadratic splitting iteration is used to solve the complex optimization problem. Experimental results demonstrate that the proposed method can achieve a superior performance compared with existing fusion methods in both subjective and objective assessments.

中文翻译：

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通过ℓ2−ℓp范数最小化的红外和可见光图像融合和去噪

摘要 传统的红外与可见光图像融合方法在采集或传输过程中往往忽略噪声，在实际应用中性能不可避免地下降。为了解决这个问题，提出了一种新的有效的变分模型来同时进行图像融合和去噪。在分别具有 p = 0 和 p = 1 的 l 2 − lp 范数最小化设置中，构建混合 l2 范数保真度项以保留来自红外和可见光图像的图像强度和细节。并且应用非凸 l0 范数和凸 l1 范数稀疏约束来减少噪声，同时保留重要的图像精细特征。此外，基于半二次分裂迭代的计算效率高的数值算法用于解决复杂的优化问题。