How to perform image enhancement on pictures using Python

How to use Python to perform image enhancement on images

Abstract: Image enhancement is one of the important steps in image processing, which can improve the quality and visual effects of images. This article will introduce how to use Python language to perform image enhancement on pictures, and attach a code example for demonstration.

1. Introduce necessary libraries and modules

Before we start, we need to introduce some necessary libraries and modules, including PIL library, numpy library and matplotlib library. These libraries provide the basic functionality required for image processing.

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

2. Reading and displaying pictures

First, we need to read a picture and display it so that we can perform image enhancement on it.

# 读取图片
img = Image.open('example.jpg')

# 显示图片
plt.imshow(img)
plt.axis('off')
plt.show()

3. Adjust the brightness of the image

Adjusting the brightness of the image is a common image enhancement method. We can adjust the brightness of the image by changing the RGB value of each pixel.

# 调整图像亮度
def adjust_brightness(img, factor):
    # 将图像转为numpy数组
    img_array = np.array(img)
    
    # 通过调整每个像素点的RGB值来改变亮度
    adjusted_array = img_array * factor
    
    # 将改变后的数组转为图像
    adjusted_img = Image.fromarray(adjusted_array.astype('uint8'))
    
    return adjusted_img

# 设置亮度调整参数
brightness_factor = 1.5

# 调整亮度并显示结果
adjusted_img = adjust_brightness(img, brightness_factor)
plt.imshow(adjusted_img)
plt.axis('off')
plt.show()

4. Adjust image contrast

Another common image enhancement method is to adjust the contrast of the image. We can adjust the contrast of the image by changing the brightness difference of the pixels.

# 调整图像对比度
def adjust_contrast(img, factor):
    # 将图像转为numpy数组
    img_array = np.array(img)
    
    # 通过调整每个像素点的亮度差值来改变对比度
    adjusted_array = (img_array - img_array.mean()) * factor + img_array.mean()
    
    # 将改变后的数组转为图像
    adjusted_img = Image.fromarray(adjusted_array.astype('uint8'))
    
    return adjusted_img

# 设置对比度调整参数
contrast_factor = 1.5

# 调整对比度并显示结果
adjusted_img = adjust_contrast(img, contrast_factor)
plt.imshow(adjusted_img)
plt.axis('off')
plt.show()

5. Apply image filter

Image filter is another common method of image enhancement, which can smooth or sharpen the image through the filter.

# 应用图像滤波器
def apply_filter(img, filter):
    # 将图像转为numpy数组
    img_array = np.array(img)
    
    # 应用滤波器
    filtered_array = np.convolve(img_array.flatten(), filter.flatten(), mode='same').reshape(img_array.shape)
    
    # 将滤波后的数组转为图像
    filtered_img = Image.fromarray(filtered_array.astype('uint8'))
    
    return filtered_img

# 设置滤波器
filter = np.array([[1, 1, 1],
                   [1, -8, 1],
                   [1, 1, 1]])

# 应用滤波器并显示结果
filtered_img = apply_filter(img, filter)
plt.imshow(filtered_img)
plt.axis('off')
plt.show()

6. Summary

This article introduces how to use Python to perform image enhancement processing on pictures. By adjusting the brightness, contrast and filters, you can improve the visual effect of your pictures. Readers can adjust parameters and filters according to actual needs to further optimize the image enhancement effect.

The above is a brief introduction to image enhancement using Python. I hope it will be helpful to readers.

References:
[1] J. Kautz, J. Wang, and P. Cohen. A naturalistic open source movie for optical flow evaluation. In European conference on computer vision, pages 611–625. Springer, 2016.
[2] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.
[3] GitHub. PyTorch. https://github.com/pytorch/pytorch, 2020.

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