Multispectral Image Enhancement Based on Weighted Principal Component Analysis and Improved Fractional Differential Mask

Compared with single-band images, multispectral images with multiple wavelengths contain more spectral information and can fully reflect the detailed features of ground objects in different bands. To synthesize the feature information, a method of multispectral image enhancement based on improved weighted principal component analysis (WPCA) and improved fractional differential (IFD) filtering is proposed. First, the optimum index factor (OIF) model is modified to select the bands for easy follow-up processing. Then an improved WPCA transform that uses the average gradient and texture roughness is applied to compensate for each band. This approach preserves the main information, compresses the amount of image data, and obtains uncorrelated principal components. According to the correlation of neighboring pixels, a new mask is introduced to enhance the first principal component. Finally, the brightness values of the image are adjusted after inverse WPCA transform to obtain the final enhanced image. The experimental results demonstrate the superiority of the proposed method over related methods. Its future practical applications are discussed in the conclusion.