Image Recognition with Preprocessing and Sharpening Techniques

During image processing, we first perform grayscale conversion using functions like rgb2gray() in MATLAB or cv2.cvtColor() in OpenCV, which helps extract image details and features by reducing color information to intensity values. Subsequently, we apply histogram equalization through algorithms that redistribute pixel intensities to enhance contrast, making images clearer and more vivid. This is typically implemented using histeq() in MATLAB or equalizeHist() in OpenCV. Additionally, we employ median filtering with kernel-based convolution operations to remove image noise while preserving edges, significantly improving image quality. These preprocessing techniques enable accurate identification of landmark objects in images through feature extraction algorithms. Furthermore, we experiment with different sharpening methods including Laplacian filters and unsharp masking, which enhance image details and contours through gradient-based operations and high-pass filtering. These comprehensive approaches provide excellent learning material for beginners, helping them better understand and master fundamental image processing techniques through practical code implementation.