A. Image sharpening in Python involves enhancing the edges and fine details in an image to improve its visual quality. It is typically achieved by emphasizing high-frequency components, such as edges, while suppressing low-frequency components. The most common approach to image sharpening is to apply a sharpening filter or kernel to the image using convolution. In Python, OpenCV provides functions to perform convolution and apply filters. Here's a general overview of how image sharpening works: 1. Load the image: Read the image using OpenCV's imread function and store it in a variable. 2. Convert the image to grayscale (optional): If desired, convert the image to grayscale using OpenCV's cvtColor function. This step is useful if you want to apply sharpening to the grayscale version of the image. 3. Create a sharpening filter/kernel: Define a kernel, which is a small matrix of values, that highlights edges and fine details. Common sharpening kernels include Laplacian, Unsharp Masking, or High Pass filters. 4. Apply the sharpening filter: Use OpenCV's filter2D function to apply the sharpening kernel to the image. The filter2D function convolves the kernel with each pixel of the image, enhancing the edges and details. 5. Adjust the sharpening strength: You can control the strength of the sharpening effect by adjusting the values in the kernel. Higher values increase the emphasis on edges, while lower values yield a milder sharpening effect. 6. Display the original and sharpened images: Use OpenCV's imshow function to display the original and sharpened images side by side. You can also save the sharpened image to a file using the imwrite function. It's worth noting that image sharpening can also be achieved using other techniques, such as gradient-based methods or frequency domain operations like Fourier Transform. However, the convolution-based approach described above is a commonly used and straightforward method for image sharpening in Python using OpenCV.