Digital image processing: Most photo restoration services use digital image processing techniques, which involve applying algorithms to enhance and restore damaged images.
Frequency domain processing: Some photo restoration services use frequency domain processing, which involves analyzing the image in the frequency domain to separate the image into its component frequencies.
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Convolutional Neural Networks (CNNs): Many AI-powered photo restoration tools use CNNs, a type of deep learning algorithm, to learn patterns in images and restore damaged areas.
Generative Adversarial Networks (GANs): Some AI-powered photo restoration tools use GANs, which consist of two neural networks that work together to generate new images or restore damaged ones.
Image noise reduction: Many photo restoration services use algorithms to reduce image noise, which is the random variation in pixel values that can degrade image quality.
Chroma subsampling: When restoring old color photos, chroma subsampling is used to reduce the amount of chroma (color) data, which can help to reduce the file size of the image.
Histogram equalization: Some photo restoration services use histogram equalization to adjust the brightness and contrast of an image, which can help to restore faded or overexposed areas.
Image interpolation: When restoring damaged images, image interpolation is used to estimate missing pixel values by analyzing neighboring pixels.
U-Net architecture: Some AI-powered photo restoration tools use the U-Net architecture, a type of neural network that is particularly well-suited for image segmentation and restoration tasks.
Depth maps: Some photo restoration services use depth maps, which are 2D representations of the 3D structure of an image, to help restore damaged areas.
Optical character recognition (OCR): Some photo restoration services use OCR to recognize and restore text in damaged images.
Image registration: When restoring multiple images of the same scene, image registration is used to align the images and remove distortions.
Non-local means denoising: Some photo restoration services use non-local means denoising, a filter that removes noise from an image by analyzing similar patches of pixels.
Wavelet denoising: Some photo restoration services use wavelet denoising, a filter that removes noise from an image by representing it in the wavelet domain.
Quality metrics: Many photo restoration services use quality metrics, such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity Index (SSIM), to evaluate the quality of the restored image.