改进多尺度结构化融合的红外与可见光图像融合OA北大核心CSTPCD

Under low-light conditions,the fusion of infrared and visible images often results in images with poor contrast,lacking in detail,and requiring a lengthy processing time.To address these issues,this pa-per introduces an enhanced multi-scale structural fusion approach.Initially,it improves the low-light visi-ble image using a dynamic range compression enhancement algorithm.Subsequently,through a multi-scale structural image decomposition method,it separates the enhanced visible and infrared images into their low-frequency and high-frequency components.For image fusion,the low-frequency components of both image types are merged using a technique based on the root mean square error coefficient.The high-fre-quency components are initially fused in a straightforward manner,followed by an optimized fusion using a self-adaptive weight adjustment based on image information entropy.Afterward,by reversing the multi-scale structural decomposition,the fused low and high-frequency components are combined to form a com-plete image.To further enhance the image contrast,a regional pixel enhancement algorithm based on gray level classification is introduced.The effectiveness of this method is compared with nine conventional infra-red and visible image fusion techniques,both qualitatively and quantitatively,using TNO and CVC-14 da-tasets.The proposed method demonstrates superior performance in terms of average gradient,cross entro-py,edge intensity,standard deviation,and spatial frequency,along with an improved overall visual quali-ty.This confirms that the images produced by the proposed method exhibit enhanced detail,clarity,con-trast,and are processed more quickly.