How to use image sharpening techniques in Python?

Image sharpening is a commonly used image processing technique that can make pictures clearer and more detailed. In Python, we can use some common image processing libraries to implement image sharpening functions. This article will introduce how to use the Pillow library, OpenCV library and Scikit-Image library in Python for image sharpening.

1. Use Pillow library for image sharpening

Pillow library is a commonly used image processing library in Python, which provides an enhanced version of PIL (Python Image Library). The Pillow library can be used to read and process various types of images, such as JPG, PNG, BMP, etc. The steps to use the Pillow library for image sharpening are as follows:

1) Install the Pillow library

Enter the following command in the command line to install the Pillow library:

pip install Pillow

2) Read Picture

Use the Image module of the Pillow library to read pictures. For example, we can read a picture named "test.jpg":

from PIL import Image

image = Image.open('test.jpg')

3) Enhance the sharpness of the picture

Use the Filter module of the Pillow library to perform sharpening operations. You can use filters such as blur, edge enhancement, and sharpness enhancement. Here we use the UnsharpMask filter to enhance the sharpness of the image:

from PIL import ImageFilter

sharpened_image = image.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))

In the above code, the radius parameter specifies the blur radius, the percent parameter specifies the sharpening percentage, and the threshold parameter specifies the sharpening threshold.

4) Save the result

Finally, use the save() method to save the result as a new picture:

sharpened_image.save('sharpened_test.jpg')

2. Use the OpenCV library for image sharpening

The OpenCV library is an open source computer vision library that can be used for a variety of image processing tasks. The steps to use the OpenCV library for image sharpening are as follows:

1) Install the OpenCV library

Enter the following command in the command line to install the OpenCV library:

pip install opencv-python

2) Read Picture

Use the imread() function of the OpenCV library to read the picture. For example, we can read a picture named "test.jpg":

import cv2

image = cv2.imread('test.jpg')

3) Enhance the sharpness of the picture

Use the Laplacian function of the OpenCV library to enhance the sharpness of the picture. The code is as follows:

import cv2

kernel_size = 3
scale = 1
delta = 0
ddepth = cv2.CV_16S

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
dst = cv2.Laplacian(gray, ddepth, ksize=kernel_size, scale=scale, delta=delta)
absdst = cv2.convertScaleAbs(dst)

In the above code, the kernel_size parameter specifies the size of the operator, the scale parameter specifies the scaling factor, the delta parameter specifies the offset, and the ddepth parameter specifies the output depth.

4) Save the result

Finally, use the imwrite() function to save the result as a new picture:

cv2.imwrite('sharpened_test.jpg', absdst)

3. Use the Scikit-Image library for images Sharpening

The Scikit-Image library is a Python image processing library that provides various image processing algorithms. The steps to use the Scikit-Image library for image sharpening are as follows:

1) Install the Scikit-Image library

Enter the following command on the command line to install the Scikit-Image library:

pip install scikit-image

2) Reading pictures

Use the io module of the Scikit-Image library to read pictures. For example, we can read a picture named "test.jpg":

from skimage import io

image = io.imread('test.jpg')

3) Enhance the sharpness of the picture

Use the transformation module of the Scikit-Image library to perform sharpening operations . Here we use the unsharp_mask() function to enhance the sharpness of the image:

from skimage import filters

sharpened_image = filters.unsharp_mask(image, radius=2, amount=1.5, multichannel=True)

In the above code, the radius parameter specifies the size of the convolution kernel, the amount parameter specifies the degree of sharpening, and the multichannel parameter specifies whether it is color. image.

4) Save the result

Finally, use the imsave() function of the io module to save the result as a new picture:

io.imsave('sharpened_test.jpg', sharpened_image)

Conclusion

This article introduces the method of image sharpening using the Pillow library, OpenCV library and Scikit-Image library in Python. These libraries provide various algorithms and functions to process images, which we can choose to use according to our needs. Image sharpening is an important part of image processing. It can improve the quality and clarity of images and has broad application prospects in practical applications.

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