Tendencies in the application of artificial intelligence in the processing of photo materials

At its core, image processing means making changes to an image in order to improve its quality or to extract information. Digital processing of photographic materials based on AI, uses computer algorithms to edit digital images. In the context of digital processing, the final product can be both the image itself and the data associated with it, such as identified objects, contours, features or masks. Today, photo processing is actively used in areas such as biometrics, video games, medical imaging, video surveillance, autonomous driving, law enforcement, and others. Here are some of the main purposes of processing photographic materials [16]:

-visualization: presents processed photos in a clear and more informative way, making invisible objects visually apparent. Image enhancement and restoration: increases clarity and improves image quality; -image search: makes it easier to identify and find specific images; -object measurement: allows to measure objects in an image;

-object recognition: identifies, classifies objects in a photo, determines their location, and analyzes the scene (Fig. 3).

Fig. 3. An example of image recognition with a watermark [14]

Now let us take a closer look at each of the stages. Image acquisition - this process involves “capturing” an image using a sensor, such as a camera, and converting it into a format suitable for further use (e.g., a digital image file). One well-known method of image acquisition is scraping. Image Enhancement - in this phase, the quality of the captured image is enhanced to extract hidden information from it for further processing. Image restoration - this phase also aims to improve the image quality by eliminating possible defects to obtain a clearer image. The process is based on the use of probabilistic and mathematical models to remove noise, blurring, missing pixels, watermarks, camera focus errors, and other problems that can negatively affect the neural network training process (Fig. 4).

Fig.4. An example of “capturing ” a photo image using a neural network sensor (created by the author in Neural Filters Photoshop)

Color image processing involves working with color images and different color spaces. Depending on the type of image, it is possible to use techniques of pseudo-color processing or processing in RGB space.

The process of developing deep learning covers the entire range of actions from data collection to integration of the developed AI model into the final system. Visualization and interpretation are the processes which allow to visualize and describe the processed images. The output of AI-based systems is a set of numbers and values that represent the information, which the model was trained to generate. Typically, deep neural networks alone do not produce output in a human-readable format.

Fig. 7. Morphological processing of photo materials using AI (performed by the author in a software environment Topaz Labs)

However, with specialized visualization tools, these numerical arrays can be transformed into images suitable for detailed analysis. The use of AI and machine learning technologies significantly increases the speed of data processing and the quality of the obtained results. Thanks to AI platforms, it is possible to perform complex tasks such as face recognition, object detection and text recognition. But in order to achieve high-quality results, it is necessary to choose appropriate tools and methods [7].

Deep neural networks containing several layers of hidden neurons face the problem of gradient vanishing, which complicates the process of error backpropagation. This problem can be mitigated by ReLU activation functions, specific initialization of weights, and the use of architectures with pass-through connections. The term “depth” in the context of deep learning refers to the number of layers in a neural network. Therefore, the concept of deep learning is often used as a synonym for deep neural networks. A neural network with three or more layers, including an input and an output, is usually classified as a deep neural network, otherwise as a basic neural network. Neural networks are a very powerful and widely used tool for input data analysis [16].

Select allow you to quickly sort and remove unwanted shots from thousands of images. Photo editing has become extremely simple and productive thanks to tools supported by AI [11]: Adobe Photoshop, Skylum or Topaz Labs. In particular, Neural Filters in Photoshop open new possibilities for AI-powered image creativity, allowing to manipulate photos in seconds - with an ease that was unimaginable just a few years ago. Since neural networks have a large number of parameters, deep networks are prone to retraining on training data, so their regularization is necessary. Regularization methods, in addition to cross-validation, include weight reduction, dropout, truncation, and dataset expansion.

Artificial intelligence has enormous potential in the field of photo processing, opening new horizons for the development of technologies, improving the quality of images and increasing the efficiency of processes. Further innovations in AI are expected to continue to impact photography, providing ever more avant-garde and user-friendly solutions.

Conclusions

The use of artificial intelligence in the processing of photo materials reveals significant progress and has already become an integral part of our daily life, which emphasizes the deep integration of neural networks into various daily tasks. This study examines in detail the wide range of techniques proposed by neural networks for restoring, editing, and preserving photographs, emphasizing the potential of AI to adapt and improve these processes according to human needs. It has been determined that AI has found its application in many aspects of photo processing, from restoring damaged images to compensating for color distortions and other defects. Modern AI technologies are able to significantly improve the quality of photos, providing opportunities to restore details, improve clarity, and correct colors, which was previously unattainable without complex manual interventions.

It has been proven that AI is aimed at imitating the work of the human brain through the creation of complex models and algorithms, which allows solving information processing tasks at a new level of efficiency. The complex architecture of neural networks, including input, output, and hidden layers, plays a key role in the learning and adaptation processes, allowing the system to improve over time. With the increase in the number of layers and the complexity of the structure, machine learning is moving into the area of deep learning, while opening up new opportunities for modeling and prediction.

Key challenges have been identified, including the problem of overtraining, and strategies to overcome them have been discussed, including model simplification, early stopping, data augmentation, regularization, and the use of dropout. The research highlights the potential for AI to radically transform the field of photography, particularly through improvements in real-time autofocus and scaling up of image editing capabilities.

References

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