Python+opencv: image restoration

Summary : OpenCV is an open source computer vision library that contains many image processing and computer vision algorithms. Image inpainting using OpenCV mainly relies on traditional image processing techniques.

OpenCV image repair method and its principle:

1. Denoising : Image denoising is the process of eliminating noise in an image and improving image quality. OpenCV provides a variety of denoising algorithms, such as Gaussian filtering, median filtering, bilateral filtering, and non-local mean denoising. These algorithms remove noise by smoothing the image while trying to preserve the edges and details of the image.

2. Color balance : In old photos, colors may gradually lose their balance over time. OpenCV can adjust the color balance of the image through techniques such as histogram equalization and contrast stretching. These methods work by changing the brightness and color distribution of an image to make it look more natural and vivid.

3. Interpolation and super-resolution : Interpolation algorithms are used to enlarge an image, increasing its resolution. OpenCV provides a variety of interpolation methods, such as nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc. These methods extend an image by interpolating new pixel values between pixels. In super-resolution tasks, OpenCV can use techniques such as convolutional sparse coding (SCSR) to increase the resolution of images.

4. Image inpainting : When there are occlusions, scratches or defects in the image, image inpainting techniques can be used to fill in the missing areas. OpenCV provides inpainting algorithms such as the inpaint function. These algorithms restore images by using information from surrounding pixels to estimate pixel values in missing regions.

5. Sharpening : Sharpening is a technique that enhances image details and edges. OpenCV provides a variety of sharpening filters, such as Laplacian filtering, high-pass filtering, etc. These filters improve image clarity by emphasizing high-frequency information in the image.

These traditional image processing methods may be effective in some cases, but they are often unable to deal with complex image corruption situations. For these tasks, deep learning techniques such as generative adversarial networks (GANs) may provide better inpainting results.

History Raiders:

python: convert color photo to black and white photo

Install dependent libraries:

pip install opencv-python

Case source code : app.py

# -*- coding: utf-8 -*-
# time: 2023/4/26 18:58
# file: main.py
# 公众号: 玩转测试开发
import cv2
import numpy as np


def color_restoration(image_path):
    # 读取图像
    image = cv2.imread(image_path, cv2.IMREAD_COLOR)

    # 将图像从 BGR 转换为 LAB
    lab_image = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)

    # 将 LAB 图像拆分为单独的通道
    l_channel, a_channel, b_channel = cv2.split(lab_image)

    # 对每个通道应用自适应直方图均衡化
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    l_channel = clahe.apply(l_channel)
    a_channel = clahe.apply(a_channel)
    b_channel = clahe.apply(b_channel)

    # 将处理后的通道重新组合为 LAB 图像
    lab_image = cv2.merge((l_channel, a_channel, b_channel))

    # 将图像从 LAB 转换回 BGR
    result_image = cv2.cvtColor(lab_image, cv2.COLOR_Lab2BGR)

    return result_image


if __name__ == "__main__":
    input_image_path = "old_photo.png"  # 替换为您的老照片路径
    output_image_path = "restored_photo.png"  # 替换为恢复后的照片路径

    restored_image = color_restoration(input_image_path)
    cv2.imwrite(output_image_path, restored_image)

Before and after comparison:
insert image description here

Note : Microsoft has an open source project called "Bringing Old Photos Back to Life". This project uses deep learning techniques (specifically, generative adversarial networks, GANs) for old photo restoration. It can deal with various problems such as wear, scratches, wrinkles, fading, etc., and can automatically repair old photos to make them look clearer and more natural.

Project GitHub warehouse link:

https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life

To run this project, you need to install the required dependencies (such as PyTorch, OpenCV, etc.), and then follow the instructions on GitHub. Ideal for restoring old photos, this project can help restore your precious memories to their best condition.

Python+opencv: image restoration

Guess you like