Professional image processing has become most common in medicine, design, and media as it performs its enhancing functions at an even higher level. Artificial intelligence has shattered the old boundaries of this field by offering completely new methods to convey this technology’s image quality and functionality.
AI has made its revolution in the field of image processing just as online paper writing service have created the difference between independent works and those created with the help of experienced assistants.
It is the assistant that should be considered AI, and it helps solve complex tasks in minutes and seconds when it could take hours and even days.
Further, we will tell you that, thanks to the discoveries and development of certain areas of cyber sciences, it became possible in principle, and the main thing is how this technology became available to everyone who wants to change the world of image processing.
Fundamentals of Artificial Intelligence in Image Processing
AI encapsulates a wide spectrum of technologies, but the most prevalent ones in image processing are those related to machine learning and neural networks.
It allows data to be stored, analyzed, and learned so that it can be used to predict or, in some cases, make decisions. In the meantime, neural networks will ensure that the AI system is the main part of the image recognition process.
Coiled neural networks (CNNs) function because they can automatically extract features from images. With CNNs, a sequence of filters is applied to raw data, which extracts a multilevel list of features from the simplest ones, like edges and textures, to the most complex ones, like objects and faces.
Popularly used image processing algorithms are generative-adversarial networks (GANs) and ResNet. GANs, on the other hand, are implemented using two neural networks, each of which has a different role: one network generates images, and the other evaluates the images and decides if they are of good enough quality or not.
AI to Improve Image Quality
The most popular and most frequently used method of using artificial intelligence in working with images is to improve their quality. Super-resolution algorithms such as SRGAN use deep learning to recover fine details and improve image clarity. These algorithms can transform blurry, low-quality photos into sharp, detailed images.
Noise removal is another important task addressed by AI. Algorithms based on convolutional neural networks can effectively remove noise while preserving important image details. Examples of such algorithms include DnCNN and other deep learning methods that outperform traditional noise removal methods.
AI also makes blur removal and clarity enhancement possible. Deep learning algorithms can analyze blurred images and recover lost details, making them clearer and more usable. Examples of such algorithms include DeblurGAN, which uses generative-adversarial networks to remove blur.
It should be said that artificial intelligence in this field is not yet wholly perfect and can “play around” by adding colors and textures to images that were not original. The human task is to enter the correct parameters and correct the program to achieve a better and more natural-looking image.
Using AI for Color Correction and Restoration
If you ask any artist or photographer what the most important parameter for correct color reproduction is, they will answer that it is white balance. This flexibly adjustable parameter differentiates natural-looking photos from non-natural or low-quality images.
DeepPhotoEnhancer is often used to adjust the white balance. The algorithm automatically detects the image parameters and makes the necessary white balance adjustments to make the colors look more natural and consistent with the composition.
Restoration of old photos is now also performed with the help of artificial intelligence and self-trained neural networks. Trained neural networks and algorithms can be considered professional restorers working with old paintings.
Specialists know what the original work looked like. They understand what reagents can be applied to avoid harming the material and in what sequence they need to act to restore colors and texture so that the image takes its former appearance.
AI algorithms, in turn, scale the image to the pixel and, at each individual point, perform restoration and improvement. This approach preserves all the fine details of the old photos, and the quality, clarity, and contrast becomes much better.
Another obvious and actively used property of trained networks is transforming black-and-white photos into color ones. Algorithms such as DeOldify and Colorful Image Colorization detect the real colors in old photos, and with the help of subtle adjustments, it is possible to restore the old image historically accurately. These algorithms are used by historians and film enthusiasts to turn classic film and retro photos into color.
AI for Creative Image Processing
Artificial intelligence helps create unique creative parodies or reproduce artists’ styles through algorithms. Neural Style Transfer is most often used for these tasks. This algorithm can manipulate the original image and create another one based on the specified parameters.
For example, you can ask the program to draw a picture of the Mona Lisa in the style of Andy Warhol or create an 8-bit pixel image from a battle picture.
In addition, many online services and image-processing programs have introduced the function of generating new images. Artificial intelligence, based on only text descriptions, creates realistic pictures that never existed. Examples of such algorithms include BigGAN and StyleGAN, which can generate images of people, animals, and objects.
Pros and Cons of Using AI for Image Processing
The benefits of using artificial intelligence for photo processing are obvious, such as automating routine tasks or simplifying the processing of finished images. For example, AI algorithms can automatically correct exposure and white balance errors, making images look more professional and attractive.
At the same time, if we talk about the creative use of AI, there are many ethical problems related to copyright and estimation of the cost of the artist’s work.
Conclusion
AI for image processing has opened many doors for artists, photographers, and designers. Photo processing has become easier, and many processes have been optimized.
At the same time, AI algorithms are still not completely accurate, and it is necessary to adjust the programs’ work, which means that human involvement in the process is still at a high level.
