An image fusion method based on fuzzy regional characteristics is proposed in this paper. After the multi-resolution decomposition of an image, k-mean clustering is firstly done for the low frequency components of the each layer to decompose the low frequency image into important region, sub important region and background region. Then, all areas of the image are fuzzificated and fusion strategies are determined according to their fuzzy membership degrees. Finally, fusion result is obtained by the reconstruction from the multiresolution image representation. Experimental results and fusion quality assessments show the effectiveness of the proposed fusion method.

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.