On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform,DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.
Region-based fusion rule is adopted in research on multi-sensor image fusion because of its better accuracy than pixel based and widow-based fusion rules. Background subtraction is a simple technique to divided image into object region and background region but its uses are limited because a set of continuous images is needed. Moreover, it’s difficult to distinguish objects from visible image without continuous images when objects are pretended. A region-based fusion rule using K-means clustering is proposed to overcome the limit stated earlier. In the proposed scheme, low frequency images produced by DT-CWT (Dual-Tree Complex Wavelet Transform) were clustered into different regions to obtain a joint map. According to local energy ratio (LER) and the size of regions, average gradient of window or ratio of region sharpness (RS) are adopted as measures respectively to fuse low frequency coefficients of different regions in joint map. The experimental result shows that the proposed approach performs better than pixel-based and window-based fusion rules in entropy, spatial frequency, average frequency and QAB/F.
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