Various Image Fusion Performance Evaluation Parameters

In this document, we propose incorporating detailed explanations about performance evaluation parameters for image fusion. These metrics are essential for quantifying the performance and quality of image fusion algorithms. Some commonly used evaluation parameters include: - D (Difference): Measures the degree of difference between the fused image and the original reference image. In implementation, this can be calculated by summing absolute pixel differences across all color channels. - MSE (Mean Squared Error): Computes the average squared difference between corresponding pixels in the fused and original images. The standard formula is MSE = (1/N)Σ(fused_pixel - original_pixel)^2, where N represents the total number of pixels. - PSNR (Peak Signal-to-Noise Ratio): Evaluates image quality by calculating the ratio between the maximum possible power of a signal and the power of corrupting noise. Typically implemented as PSNR = 20·log10(MAX_I/√MSE), where MAX_I is the maximum possible pixel value. - SF (Structural Similarity): Assesses the structural similarity between fused and original images using luminance, contrast, and structure comparisons. The SSIM index implementation involves sliding window computation across the image. - RMSE (Root Mean Squared Error): Calculates the square root of MSE, providing error measurement in the same units as the original data. Computed as RMSE = √MSE. - NCD (Normalized Color Difference): Measures normalized color differences in CIELAB color space, particularly important for color image fusion evaluation. - REL (Relative Error): Computes the relative pixel-wise error between fused and reference images, often implemented as |fused - reference|/|reference| for each pixel location. - MI (Mutual Information): Quantifies the statistical dependency or information similarity between fused and source images, requiring joint probability distribution calculations. - MAE (Mean Absolute Error): Calculates the average absolute difference between corresponding pixels using MAE = (1/N)Σ|fused_pixel - original_pixel|. - DREL (Dynamic Relative Error): Evaluates dynamic range preservation by measuring relative errors under different illumination conditions or exposure levels. - EOG (Edge Orientation Gradient): Assesses edge preservation by comparing gradient magnitudes and orientations between fused and original images using Sobel or Canny edge detection operators. - CREF (Color Fidelity): Measures color preservation accuracy, particularly crucial for multispectral or color image fusion applications. These evaluation parameters provide comprehensive insights into image fusion algorithm performance, enabling objective assessment and comparison of different fusion methodologies. Implementation typically involves pixel-wise operations, statistical calculations, and spatial analysis techniques that can be efficiently coded using image processing libraries like OpenCV or MATLAB's Image Processing Toolbox.