How can I sharpen an image in OpenCV?

You can try a simple kernel and the filter2D function, e.g. in Python:

kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
im = cv2.filter2D(im, -1, kernel)

Wikipedia has a good overview of kernels with some more examples here - https://en.wikipedia.org/wiki/Kernel_(image_processing)

In image processing, a kernel, convolution matrix, or mask is a small matrix. It is used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between a kernel and an image.

You can sharpen an image using an unsharp mask. You can find more information about unsharp masking here. And here's a Python implementation using OpenCV:

import cv2 as cv
import numpy as np

def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
    """Return a sharpened version of the image, using an unsharp mask."""
    blurred = cv.GaussianBlur(image, kernel_size, sigma)
    sharpened = float(amount + 1) * image - float(amount) * blurred
    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
    sharpened = sharpened.round().astype(np.uint8)
    if threshold > 0:
        low_contrast_mask = np.absolute(image - blurred) < threshold
        np.copyto(sharpened, image, where=low_contrast_mask)
    return sharpened

def example():
    image = cv.imread('my-image.jpg')
    sharpened_image = unsharp_mask(image)
    cv.imwrite('my-sharpened-image.jpg', sharpened_image)

One general procedure is laid out in the Wikipedia article on unsharp masking:

You use a Gaussian smoothing filter and subtract the smoothed version from the original image (in a weighted way so the values of a constant area remain constant).

To get a sharpened version of frame into image: (both cv::Mat)

cv::GaussianBlur(frame, image, cv::Size(0, 0), 3);
cv::addWeighted(frame, 1.5, image, -0.5, 0, image);

The parameters there are something you need to adjust for yourself.

There's also Laplacian sharpening, you should find something on that when you google.