Advances in Artificial Intelligence for Image Processing: Techniques, Applications, and Optimization

Introduction

AI has a huge influence on image processing by providing cutting-edge techniques and applications. The basics of image processing, representation, formats, enhancement techniques, filtering, machine learning, neural networks, optimization techniques, digital watermarking, picture security, cloud computing, image augmentation, and data pretreatment are covered in this chapter. It also talks about how platforms, performance, privacy, and security are affected by cloud computing. Future developments and applications will demonstrate the important advances AI has achieved in image processing while simultaneously tackling moral and societal issues(Anitha et al., 2023; Reddy et al., 2023). An important development in image processing is deep learning, a subfield of AI that makes use of artificial neural networks. Convolutional Neural Networks (CNNs) have demonstrated effectiveness in a variety of tasks, including segmentation, object recognition, and image categorization. These deep learning architectures achieve excellent generalisation and accuracy levels by extracting key features from raw visual input(Alam et al., 2022).

A generator and discriminator neural network are combined to create GANs, which are AI image processing methods. They have important uses in a variety of fields, including healthcare, where they enhance diagnostic accuracy, early sickness detection, and customised treatment plans. In dermatology, pathology, and radiology, AI has also excelled in identifying abnormalities, diseases, and medical professionals. Robots are now able to recognise and evaluate visual information like humans thanks to AI in image processing(Janardhana, Anushkannan, et al., 2023; Jeevanantham et al., 2023; Selvakumar et al., 2023). AI algorithms are used by autonomous vehicles for safe navigation, and surveillance systems monitor activity and boost safety. The entertainment industry has benefited from AI's expansion of creativity by making content production, video editing, and special effects possible. In order to increase the efficacy and efficiency of algorithms, optimization is essential in AI image processing. Optimizing the training and inference procedures is crucial as the complexity of deep learning models rises. Deep neural network training is accelerated by methods including parallel computing, distributed learning, and hardware acceleration, enabling the deployment of real-time applications on devices with limited resources(Letourneau-Guillon et al., 2020; Malik et al., 2018).

The utilisation of computational resources like memory and energy efficiently is necessary for improved AI algorithms for processing images. Model compression techniques like pruning, quantization, and knowledge distillation reduce the size of deep learning models and the amount of compute required without significantly reducing performance. The deployment of AI-powered image processing apps on a variety of platforms, including edge devices and cloud-based infrastructure, is made possible by these optimization approaches(Khokhar et al., 2015).