Several Image Fusion Evaluation Metrics

Image fusion evaluation metrics are essential tools for assessing the quality of fused images, measuring fusion performance from different perspectives. Below are common metrics and their significance with code implementation considerations: Average Gradient (AG): Reflects image sharpness and edge information. Higher AG values indicate better preservation of image details. Implementation typically involves calculating gradient magnitudes using operators like Sobel or Prewitt. Information Entropy (EN): Measures the richness of information in an image. Higher entropy values suggest greater information content in the fused image. Computed by analyzing the probability distribution of pixel intensities. Feature Mutual Information (FMI): Evaluates feature correlation between fused and source images. Higher values indicate more complete feature transfer. Often implemented using feature extraction techniques followed by mutual information calculation. Mutual Information (MI): Quantifies the shared information between fused and source images. Larger values correspond to better fusion performance. Computed by comparing joint and marginal histograms of image pairs. Edge Information Preservation Index (Qab): Measures the preservation of edge structures. Higher values denote less edge information loss. Implementation involves edge detection algorithms and similarity assessment. Weighted Fusion Quality Evaluation Factor (Qw): Comprehensively evaluates fusion quality by considering both structural and luminance information. Typically incorporates multiple metrics with optimized weighting schemes. Spatial Frequency (SF): Reflects local variations and texture richness in images. Higher SF values indicate more high-frequency components. Calculated using root mean square of row and column frequency components. Structural Similarity Index (SSIM): Assesses structural similarity by considering luminance, contrast, and structural information. Values closer to 1 indicate superior fusion quality. Implementation uses local window-based comparisons. These metrics, when used collectively, provide comprehensive evaluation of fusion algorithm performance, ensuring results are both visually clear and information-rich. Code implementations often involve matrix operations, statistical calculations, and multi-scale analysis techniques.