针对近红外与彩色可见光图像融合后出现的对比度降低、细节丢失、颜色失真等问题, 提出一种基于 Tetrolet变换和自适应脉冲耦合神经网络 PCNN(PCNN-Pulse Coupled Neural Network)的近红外与彩色可见光图像融合的新算法。首先将彩色可见光源图像转换到各个分量相对独立的 HSI空间(HSI-Hue Saturation Intensity), 将其亮度分量与近红外图像进行 Tetrolet分解, 对分解后得到的低频系数, 提出一种从给定不完备数据集中寻找潜在分布最大似然估计的期望最大算法融合规则；对分解后得到的高频系数, 采用一种 Sobel算子自动调节阈值的自适应 PCNN模型作为融合规则；处理后的高低频图像经 Tetrolet逆变换作为融合后的亮度图像, 提出一种饱和度分量自适应拉伸方法来解决图像饱和度下降的问题。处理后的各个分量反向映射到 RGB空间, 完成融合。将本文算法与多种高效融合算法进行对比分析, 实验表明, 本方法取得的图像, 细节清晰, 色彩对比度得到提升, 在图像饱和度、颜色恢复性能、结构相似性和对比度等客观评价指标上均具有明显的优势。

To address the problems of low contrast, loss of detail, and color distortion after near-infrared and color visible image fusion, an algorithm for near-infrared and color visible image fusion based on the Tetrolet transform and pulse coupled neural network (PCNN) is proposed. First, the color visible light source image is transformed into a hue saturation intensity space, where each component is relatively independent, and its brightness component is decomposed into a near-infrared image by Tetrolet decomposition. Subsequently, a fusion rule for expectation maximization likelihood estimation of the potential distribution from a given incomplete dataset is proposed. A self-adaptive PCNN model with a Sobel operator that automatically adjusts the threshold is used as a fusion rule, and the processed high-frequency and low-frequency images are fused by Tetrolet inverse transformation as brightness images. An adaptive stretching method for the saturation component is proposed to solve the problem of image saturation decline. The processed components are mapped back to red–green–blue space to complete the fusion. The proposed algorithm was compared with several efficient fusion algorithms. The experimental results show that the image obtained by this method has clear details and improved color contrast. It has obvious advantages in image saturation, color restoration performance, structural similarity, and contrast.