Edge Strength Calculation and Comprehensive Image Analysis Toolkit

This MATLAB code primarily calculates image edge strength using gradient-based operators (like Sobel or Canny), information entropy through histogram probability distributions, grayscale mean via pixel intensity averaging, standard deviation (MSE implementation for variance calculation), root mean square error, peak signal-to-noise ratio (PSNR) for quality assessment, spatial frequency (SF) using Fourier domain analysis, image sharpness metrics, mutual information (MI) for statistical dependency measurement, structural similarity (SSIM) with luminance, contrast and structure comparisons, cross entropy calculations, and relative standard deviation normalization. Additionally, the code computes image gradients through convolution kernels, color histograms using binning algorithms, image contrast via dynamic range analysis, and image smoothness through texture variance measurements. These features enable deep image characteristic analysis and provide comprehensive data for evaluating image quality and clarity. The toolkit also implements various filtering techniques including mean filtering with neighborhood averaging, median filtering for salt-and-pepper noise removal, and Gaussian filtering for smooth noise reduction. These filtering methods help eliminate image noise and enhance overall image quality and sharpness. In summary, this MATLAB code offers multiple functionalities and features that facilitate thorough image metric analysis and evaluation, enabling users to draw accurate conclusions and judgments through robust algorithmic implementations.