Evaluating Image Fusion Results

To evaluate image fusion results comprehensively, the following metrics can be employed:

- Mean: Measures the average brightness level of an image. In implementation, this can be calculated using numpy's mean() function on pixel intensity values across all channels.

- Standard Deviation: Quantifies the dispersion of pixel value distribution. Computationally, this involves determining how much pixel values deviate from the mean, typically implemented through statistical variance calculations.

- Entropy: Assesses the information content and complexity of an image. Algorithmically, this involves calculating the Shannon entropy based on the probability distribution of pixel intensities, often implemented using histogram analysis.

- Gradient: Evaluates edge strength and texture characteristics. This can be computed using gradient operators like Sobel or Prewitt filters to detect intensity changes across the image.

- Correlation Coefficient: Measures the linear relationship between corresponding pixels in different images or channels. Implementation typically involves covariance calculations normalized by the standard deviations of the compared datasets.

- Spectral Distortion: Quantifies the deviation in color distribution, particularly important for multispectral fusion. This metric often requires specialized color space transformations and distance calculations between spectral signatures.

These metrics collectively provide a multidimensional assessment of fusion quality, covering luminance characteristics, structural information, statistical properties, and color fidelity.