Normalization Processing of Fingerprint Grayscale Images

I successfully performed normalization processing on fingerprint grayscale images using MATLAB software, achieving excellent results. The implementation involved a systematic approach to ensure image quality and accuracy. Initially, I conducted preprocessing operations including noise removal using median filtering and image smoothing through Gaussian filters to enhance image quality. Subsequently, I applied a specialized normalization algorithm that adjusts pixel intensity values to a standard range, typically using mean and variance calculations to ensure consistent comparison between different fingerprint images. The algorithm implementation involved calculating the global mean and standard deviation of the image matrix, then applying linear transformation to normalize pixel values to a predefined range (usually [0,255] for 8-bit images). Key MATLAB functions utilized included imread() for image loading, medfilt2() for noise reduction, and custom normalization functions implementing statistical transformations. Finally, I evaluated the processed images using quantitative metrics such as peak signal-to-noise ratio (PSNR) and qualitative visual assessment, both yielding satisfactory outcomes. This process demonstrates the effectiveness and reliability of MATLAB software in fingerprint image processing applications, particularly through its comprehensive image processing toolbox and flexible programming environment for implementing custom normalization algorithms.