Image recovery Automatic translate

Below we describe a number of methods aimed at improving the visual quality of images. These techniques and technologies address a variety of defects, some of which can be corrected programmatically using classical image processing algorithms, while others require sophisticated neural network capabilities. Using online services, some images can be enhanced for free , but complex cases may require professional software or a subscription.

Image pre-processing

This is the first step towards improving the image. This step involves normalizing the image data to prepare it for further processing. Typical pre-treatment components:

1. Grayscale conversion : Many image enhancement algorithms work on single-channel images. Converting a color image to grayscale simplifies subsequent processing steps.

2. Noise reduction : Noise, or random fluctuations in pixel values, can degrade image quality. Denoising algorithms such as Gaussian blur or median filtering, as well as more complex algorithms for finding repeating small distortions, help reduce these unwanted fluctuations.

3. Histogram equalization : This technique improves the contrast of an image by distributing the most frequently occurring intensity values. It improves details in both dark and light areas of the image, and sets the center of the light balance to the highest point so that the number of pixels lighter and darker than the future middle is approximately equal.

Spatial techniques

These methods directly manipulate pixel values ​​to improve image quality. They are effective for certain types of defects:

1. Sharpening : Enhances the edges of objects in an image, making their boundaries appear clearer. To enhance sharpness, techniques such as the Laplacian filter or unsharpness masking are commonly used.

2. Anti-aliasing : Reduces fine detail and noise by averaging the values ​​of pixels in the surrounding area. Common methods include Gaussian blur and two-way filtering.

3. Contrast Correction : Enhances the differences between light and dark areas. Linear contrast stretching and adaptive histogram equalization are typical methods used for contrast adjustment.

Frequency methods

Such techniques operate with the Fourier transform. These methods are useful for eliminating periodic noise and enhancing certain frequency components:

1. Fourier transform : Converting an image into the frequency domain allows its frequency components to be manipulated. Low-pass filters can reduce high-frequency noise, while high-pass filters can improve edges and fine detail.

2. Wavelet transform : This technique provides image analysis at multiple resolutions. It is especially useful for noise reduction and compression, allowing you to selectively enhance image characteristics at different scales.

Image restoration

Restoring the original image from its degraded version. Inverse filtering and deconvolution methods are often used for this:

1. Inverse filtering : This is an attempt to reverse the effect of a known degradation function. For example, if an image is blurry due to known motion, inverse filtering can help restore sharpness.

2. Wiener filtering : This technique balances noise reduction and preservation of image detail. It is especially effective when the degradation and noise characteristics are known, such as in the case of Jpeg compression.

Defects correctable by software

Some image defects can be effectively corrected using traditional algorithms:

1. Gaussian noise : This type of noise can be reduced using filters such as Gaussian blur or median filtering.

2. Motion Blur : Prominent motion blur can be partially reduced using deconvolution techniques such as Wiener filtering.

3. Uneven lighting : Histogram equalization and contrast stretching can correct uneven lighting.

Defects requiring the use of neural networks

Neural networks cope well with complex problems of improving images that are difficult to solve using traditional methods, but it should be understood that the point of neural networks is to replenish data by generating them and the resulting image may not correspond to the original (lost) original in full or in part.

1. Super-resolution : Neural networks can generate high-resolution images from low-resolution source data, enhancing details beyond the capabilities of traditional interpolation methods.

2. Image Inlay : Neural networks can fill in missing or damaged parts of an image, using information about surrounding pixels to create believable content.

3. Colorization : Converting grayscale images to color requires understanding the context and semantics of the scene, a task well suited to neural networks.

4. Blur : While some types of blur can be dealt with using inverse filtering, complex motion blur often requires the sophisticated capabilities of neural networks to accurately restore sharpness.

Neural network architectures

Several neural network architectures have proven effective in image enhancement tasks:

1. Convolutional Neural Networks : Widely used for a variety of image processing tasks, including denoising, upscaling (effectively increasing resolution), and inpainting.

2. Generative Adversary Networks (GANs) : GANs consist of two networks—a generator and a discriminator—that compete with each other to produce realistic images. They are especially effective for creating high-quality images and filling in missing parts.

3. Autoencoders : These networks encode an image into a lower dimensional representation and then decode it back to its original resolution. Variational autoencoders (VAEs) add a probabilistic element, making them useful for generating varied and plausible image enhancements.

Step-by-step image enhancement algorithm

The combination of traditional methods with neural network approaches allows us to obtain a reliable image improvement algorithm:

1. Image Processing : If necessary, grayscale the image and apply noise reduction techniques.
2. Apply spatial processing techniques : Use sharpening, smoothing, and contrast adjustment techniques to improve key image characteristics.
3. Frequency domain transform : Apply Fourier transforms or wavelet transforms to remove periodic noise and enhance specific frequency components.
4. Image restoration : Using inverse or Wiener filtering to correct known degradations.
5. Enhancement with Neural Networks : Trained neural network models are used to solve complex problems such as super-resolution, impregnation, colorization and blurring.

Each technique is aimed at eliminating certain types of image defects, providing comprehensive improvement. Traditional methods are effective at dealing with simple defects, while neural networks provide the complex processing and refinement needed to solve more complex problems. This comprehensive approach provides high-quality image enhancement suitable for a wide range of applications.