Performing Digital Image Processing with MATLAB for Grid Pattern Noise Removal

In digital image processing, MATLAB can be employed to eliminate grid-like stripe noise from images. The filtering performance is generally satisfactory, though certain post-processing steps may be necessary. During post-processing, additional techniques or algorithms can be applied to further enhance image quality. For instance, image enhancement methods such as histogram equalization or contrast stretching can be implemented using MATLAB's imadjust() or histeq() functions to improve image clarity and contrast. Furthermore, edge detection algorithms like Sobel, Prewitt, or Canny operators can be utilized through edge() function to extract boundary information from the image. By applying multiple processing stages sequentially - starting with noise filtering using spatial domain filters (median filtering with medfilt2()) or frequency domain approaches (FFT-based filtering with fft2() and ifft2()), followed by enhancement and feature extraction - image quality can be progressively improved to meet specific application requirements. The implementation typically involves working with MATLAB's Image Processing Toolbox functions for reading images (imread()), converting color spaces (rgb2gray()), and applying various filter operations.