How to use AI technology to restore old photos (with examples and code analysis)

Old photo restoration is a method of using artificial intelligence technology to repair, enhance and improve old photos. Using computer vision and machine learning algorithms, the technology can automatically identify and repair damage and flaws in old photos, making them look clearer, more natural and more realistic.

The technical principles of old photo restoration mainly include the following aspects:

1. Image denoising and enhancement

When restoring old photos, they need to be denoised and enhanced first. Image processing algorithms and filters, such as mean filtering, Gaussian filtering, bilateral filtering, etc., can be used to solve noise and color spots problems, thereby improving the quality of photos.

2. Image restoration and repair

In old photos, there may be some defects and damage, such as scratches, cracks, fading, etc. . These problems can be solved by image restoration and repair algorithms. Commonly used algorithms include texture-based image repair algorithms, region-based image repair algorithms, interpolation-based image repair algorithms, etc. These algorithms can automatically restore missing parts of a photo by learning patterns and characteristics of surrounding pixels.

2. Image reconstruction and super-resolution

For some old photos with lower resolution, image reconstruction and super-resolution can be used algorithms to improve its clarity and detail. This can be achieved by using deep learning networks and convolutional neural networks, such as SRCNN, ESPCN, SRGAN, etc. These algorithms can automatically convert low-resolution images into high-resolution images by learning the mapping relationship between high-resolution images and low-resolution images.

3. Color restoration and correction

Old photos may also have color distortion and fading problems, which require color restoration and correction. This can be achieved by utilizing color balance and automatic white balance algorithms, such as automatic white balance algorithms based on grayscale world assumptions, color balance algorithms based on histogram equalization, etc. These algorithms can automatically adjust the color distribution and brightness of an image to make it look more natural and realistic.

The following is a sample code for old photo restoration using Python and the OpenCV library:

import cv2

# 读取老照片
img = cv2.imread('old_photo.jpg')

# 图像去噪和增强
img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
img = cv2.equalizeHist(img)

# 图像修复
mask = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = cv2.threshold(mask, 220, 255, cv2.THRESH_BINARY)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
mask = cv2.erode(mask, None, iterations=4)
mask = cv2.dilate(mask, None, iterations=4)
mask = cv2.medianBlur(mask, 9)
img = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)

# 图像重建和超分辨率
sr = cv2.dnn_superres.DnnSuperResImpl_create()
sr.readModel('espcn_x3.pb')
sr.setModel('espcn', 3)
img = sr.upsample(img)

# 颜色还原和校正
img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
img = cv2.split(img)
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
img[0] = clahe.apply(img[0])
img = cv2.merge(img)
img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)

# 显示修复后的照片
cv2.imshow('Restored Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code uses various image processing functions in the OpenCV library and Algorithm realizes various steps of old photo restoration. Specifically, the code uses the fastNlMeansDenoisingColored() function and equalizeHist() function for image denoising and enhancement, the inpaint() function for image repair, and the DnnSuperResImpl_create() function and upsample() function for image reconstruction and super. resolution, and used the createCLAHE() function and apply() function for color restoration and correction.

Among them, the image repair part uses a region-based image repair algorithm. By constructing a mask, performing morphological operations and median filtering, it achieves the restoration of noise and defects in photos. of repair. The image reconstruction and super-resolution part uses the ESPCN algorithm to convert low-resolution images into high-resolution images, thereby improving the clarity and details of the photos. The color restoration and correction part uses a color balance method based on the CLAHE algorithm to convert the image to LAB color space, and applies the CLAHE algorithm on the brightness channel for color restoration and correction.

In practical applications, it is necessary to select appropriate algorithms and parameters according to the specific conditions and needs of the photo to achieve the best results.

In short, old photo restoration is a complex image processing technology that requires a combination of multiple algorithms and technologies to achieve. In practical applications, it is necessary to select appropriate algorithms and parameters according to the specific conditions and needs of the photo to achieve the best results.

The above is the detailed content of How to use AI technology to restore old photos (with examples and code analysis). For more information, please follow other related articles on the PHP Chinese website!