基于自适应加权的多尺度图像融合研究

近年来图像融合技术广泛应用到电力行业，通过不同类型的图像传感器采集电力设备和输电线的图像，经过红外和可见光的图像融合处理，实现电力设备及输电线的智能巡视和故障分析。文中提出一种基于自适应加权的多尺度图像融合算法，采用配准后的可见光和红外图像，进行多尺度小波分解，根据高低频的不同图像特征，低频采用自适应加权融合规则，高频采用绝对值最大的融合规则，将融合后的小波系数进行逆变换后得到全新的融合图像。通过对融合图像的主观和客观评价分析，证明融合算法解决了单一图像传感器采集图像存在的完整性问题，提高了融合图像细节信息，提升了场景的置信度。

Multi-scale Image Fusion Based on Adaptive Weighting

In recent years, image fusion technology has been widely used in the power industry. Different types of image sensors are used to collect images of power equipment and transmission lines. Through the fusion of infrared and visible light images, intelligent inspection and fault analysis of power equipment and transmission lines can be realized. This article first briefly introduces common image fusion algorithms and fusion image evaluation standards. A multi-scale image fusion algorithm based on adaptive weighting is proposed, which uses the registered visible light and infrared images to perform multi-scale wavelet decomposition. According to the different image characteristics of high and low frequencies, the low frequency adopts the adaptive weighted fusion rule and the high frequency adopts the fusion rule with the largest absolute value. The fused wavelet coefficients are inversely transformed to obtain a new fused image. Subjective and objective evaluation and analysis of the fusion image confirm that the fusion algorithm solves the integrity problem of the image collected by a single image sensor, enhances the detailed information of the fusion image, and improves the confidence of the scene.