Algorithm for Fused Image Quality Assessment

This program implements the fusion image quality assessment algorithms presented in the papers "A NEW QUALITY METRIC FOR IMAGE FUSION" and "Object image fusion performance measure." The algorithm leverages advanced image processing techniques and mathematical models to accurately evaluate fused image quality by analyzing image features and pixel information, while comprehensively considering multiple aspects such as image clarity, contrast, and saturation. In the paper "A NEW QUALITY METRIC FOR IMAGE FUSION," the authors propose a novel image quality assessment metric that considers not only pixel-level similarity but also structural and perceptual consistency. Through experimental evaluation of multiple image fusion algorithms, this metric demonstrates excellent accuracy and stability in assessing fused image quality. The code implementation includes functions for calculating structural similarity indices and perceptual consistency metrics through multi-scale image analysis. The paper "Object image fusion performance measure" introduces a specialized image quality assessment algorithm tailored for target detection and recognition tasks in object image fusion. This algorithm evaluates fusion performance through multiple dimensions including target detection rate, target edge clarity, and target shape preservation. Experimental results show that this algorithm achieves high accuracy and robustness in object-oriented fusion assessment. The implementation incorporates edge detection algorithms and shape preservation metrics using contour analysis techniques. Therefore, this program implements algorithms from both papers, enabling accurate assessment of fused image quality while meeting requirements across different application scenarios. The code structure includes modular functions for metric calculation, result visualization, and comparative analysis. Whether in image fusion technology research or practical applications, this program holds significant importance and value for performance evaluation and algorithm validation.