Machine Image Fusion Based on Match Measure and Multiwavelet Transform of Principal Component Analysis Transform

An image fusion algorithm with match measure and multiwavelet transform of principal component analysis transform is proposed through the researching of several image fusion basic methods. Then infrared and visible images are sampled and applied by multi wavelet decomposition, matching degree is calculated to determine the wavelet coefficients of the fused image. Then the multi wavelet is reconstructed, and the result is applied by the original pretreatment. Thus, the fusion image is obtained, and the first principal component is gotten. The fusion process involves combining the fused components with other elements using the inverse PCA transformation to produce the final fused image. This image is then reconstructed to achieve the fusion outcome. The resulting fusion image is assessed based on both subjective and objective criteria. Experimental results indicate that the algorithm yields superior fusion results, as evidenced by both subjective visual quality and objective statistical metrics.