Various Parameters in Image Fusion

Image fusion is a crucial technique in computer vision and image processing that combines information from multiple source images to create a single higher-quality composite image. To evaluate the effectiveness of fusion algorithms, researchers have developed various performance metrics. These metrics assess fusion quality from multiple perspectives, including sharpness, information content, noise level, and structural similarity.

Average Gradient: Reflects the clarity of image edges and details - higher values indicate sharper images. (Code implementation typically involves calculating gradient magnitudes using Sobel or Prewitt operators across the image grid)

Correlation Coefficient: Measures the similarity between fused and source images - values closer to 1 indicate stronger correlation. (Can be computed using covariance and standard deviation calculations between image pixel arrays)

Information Entropy: Evaluates the information content of an image - higher entropy values indicate richer information in the fused image. (Implementation involves calculating the probability distribution of pixel intensities and applying Shannon's entropy formula)

Mutual Information: Quantifies the shared information between source and fused images - higher values indicate better preservation of source image information. (Algorithm computes the joint probability distribution between images using histogram-based methods)

Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR): Assess image noise levels - higher PSNR values indicate better image quality. (PSNR calculation typically involves mean squared error computation relative to maximum possible pixel value)

Mean Squared Error (MSE) and Root Mean Squared Error (RMSE): Measure differences between fused and original images - smaller values indicate smaller errors. (Straightforward implementation involving pixel-wise difference squared and averaging operations)

Spatial Frequency: Reflects spatial detail variations in images - higher values indicate richer details. (Computed as the root mean square of row frequency and column frequency components)

Standard Deviation and Mean: Measure image contrast and brightness distribution. (Basic statistical calculations applied to pixel intensity arrays)

Distortion Degree and Deviation Index: Evaluate whether fusion causes distortion or deformation in the resulting image. (Specialized metrics often involving complex geometric or spectral comparisons)

These metrics are typically used in combination to comprehensively evaluate fusion algorithm performance. Different application scenarios may prioritize specific metrics - for example, medical image fusion often emphasizes information entropy and mutual information, while remote sensing image fusion may focus more on spatial frequency and signal-to-noise ratio. Selecting appropriate evaluation metrics is essential for optimizing fusion algorithms.