Using Artificial Intelligence to Refine the Implementation Trajectory of Digital Image Processing Technology

: Artificial intelligence introduces a fresh research perspective to digital image processing. However, its integration into the curriculum of colleges and universities for teaching digital image processing remains scarce. This lack of incorporation results in outdated course content, reliance on singular teaching methods, and simplistic course experiments, consequently impeding effective teaching and hindering the development of well-rounded and innovative individuals. Digital image processing technology expands the horizons of communication engineering, facilitating more convenient modes of communication for people. For instance, video calls and photo transmissions diversify everyday communication methods, transcending the constraints of time and space by enabling online meetings and fostering enhanced communication possibilities. Despite these advancements, numerous challenges and methodologies merit thorough exploration. Therefore, this paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.


Introduction
Image processing encompasses various techniques aimed at manipulating and interpreting image data, with a particular focus on image recognition and understanding.It involves the extraction of information through the reprocessing of images or videos.Everyday examples of image processing techniques include image magnification, reduction, rotation, and color correction.For instance, image inversion can be achieved using the formula s=L-1-r, where the image gray level is represented by values ranging from 0 to L-1.More sophisticated operations involve image recognition, analysis, manipulation, and other advanced techniques.These processes are essential not only in everyday tasks but also in research and interpretation endeavors.By employing image processing technologies, researchers and practitioners can extract valuable insights and information from visual data, enabling a deeper understanding of complex phenomena and facilitating innovative solutions in various fields.Through continuous advancements in image processing methodologies, the potential for groundbreaking discoveries and transformative applications continues to expand, driving progress and innovation across diverse domains.
Moreover, image processing plays a pivotal role in a wide array of industries and applications.In healthcare, for instance, medical imaging techniques such as MRI and CT scans heavily rely on image processing algorithms for accurate diagnosis and treatment planning [1][2].Similarly, in satellite imaging and remote sensing, image processing techniques are indispensable for analyzing Earth's surface features, monitoring environmental changes, and supporting disaster management efforts.In the field of security and surveillance, image processing technologies are utilized for facial recognition, object detection, and anomaly detection, enhancing public safety and crime prevention measures.Additionally, in the realm of entertainment and multimedia, image processing techniques are employed for special effects creation, video editing, and image enhancement, enriching the visual experience for audiences worldwide.
Despite the vast potential and applications of image processing, the integration of artificial intelligence (AI) has unlocked new avenues for innovation and advancement in this field [3].AI-driven approaches, particularly deep learning algorithms, have demonstrated remarkable capabilities in tasks such as image recognition, segmentation, and classification.

Digital Image out Technology
Currently, the primary applications of TENG (Triboelectric Nanogenerator) are twofold: serving as an energy source for sensors and functioning as self-actuated sensors themselves.TENG-based sensors encompass various types, including sound sensors, pressure sensors, self-actuated sensors for detecting moving objects, vibration sensors, environmental sensors with self-actuation capabilities, and biomedical sensors.These sensors can collect diverse types of data, such as audio, image, graphic, amplitude, spatial position, temperature, and frequency information, among others [5][6].The complexity and diversity of data sources pose challenges in processing and analysis.With the rapid advancement of machine learning technology, an increasing number of individuals are turning to machine learning techniques to handle vast and intricate sensor data.Machine learning, a branch of artificial intelligence, involves constructing automated learning models and iteratively optimizing them using large datasets to make informed predictions guided by algorithms.The workflow of machine learning typically includes data preprocessing, feature extraction, model building, validation, and output generation.
Digital image processing is a multidisciplinary field with applications spanning biomedicine, satellite remote sensing, hyperspectral imaging, crop monitoring, and security surveillance.In recent years, spurred by government attention and active engagement from scientific researchers, artificial intelligence technology has gained widespread adoption across various domains, including digital image processing and computer vision.Machine learning techniques have particularly excelled in applications such as image denoising, restoration, reconstruction, segmentation, and recognition.These advancements underscore the growing synergy between artificial intelligence and digital image processing, leading to significant breakthroughs and advancements in various fields.
At the same time, the demand for digital image processing talents in enterprises and scientific research institutions has increased dramatically.Being able to master the traditional digital image processing methods and related artificial intelligence technologies at the same time can lay a solid foundation for students to engage in related work, further study and research related topics after graduation.As a professional course or elective course of computer and electronic information, digital image processing has the characteristics of strong comprehensiveness, many pilot courses, high practice requirements and fast algorithm update.There are higher requirements for teachers' knowledge reserve, teaching ability and students' professional foundation.Although artificial intelligence has been widely used in the field of image processing, it is rarely involved in the teaching of colleges and universities at present, and the training of talents lags behind the development of advanced technologies and cannot adapt to the characteristics of The Times.

Deep Learning
The advent of deep learning algorithms at the onset of the 21st century significantly bolstered machines' capacity to tackle intricate problems, propelling artificial intelligence technology onto a trajectory of rapid advancement.The integration of generative artificial intelligence further underscores its remarkable capabilities in content generation, dialogue comprehension, and sequential task execution, presenting vast potential for educational applications while also posing numerous challenges for future human-machine collaboration.In the swiftly evolving landscape of artificial intelligence, the proficiency in deep learning has progressively emerged as a pivotal factor for humans to navigate complex challenges and achieve high-quality development.To establish a scientific definition and effective measurement of deep learning proficiency, this paper employs a literature research methodology to propose a conceptual framework encompassing three domains: individual cognition, interpersonal coordination, and self-regulation, along with six dimensions of critical thinking and creative thinking abilities.However, the stable conceptual framework for deep learning proficiency remains elusive due to the continual evolution of its connotations, leading to a lack of breakthroughs in the evaluation and cultivation of deep learning proficiency.The pervasive issue of students prioritizing exam-oriented learning and exhibiting weak problem-solving skills persists despite these efforts.

Image Processing Technology
The field of digital image processing is characterized by its strong comprehensiveness and interdisciplinary nature, placing high demands on students' practical skills.Central to this technology is image acquisition, a crucial component in the information acquisition process.Visual information, being a fundamental source of knowledge, plays a pivotal role in human development.For students majoring in computer science and technology, digital image processing serves as a vital compulsory course, while for majors in communication engineering and related fields, it represents an essential elective course.
Digital image processing serves as a highly comprehensive interdisciplinary field, bridging computer science, sensor technology, signal processing, optics, mathematics, and other related disciplines.Primarily, it delves into the theoretical principles and methodologies behind the utilization of computer technology for tasks such as noise removal, feature extraction, image enhancement, segmentation, restoration, and other pertinent aspects [7].
Image enhancement constitutes a ubiquitous aspect of digital image technology in our daily lives.Essentially, it involves adjusting specific content within an image by enlarging or reducing it.Unlike image scaling, which alters the overall size of an image, image enhancement operates within the pixel range, achieving its effects by increasing or decreasing individual pixels.Within digital image technology, the two most prevalent methods of enhancement are spatial and frequency domain enhancement.Spatial domain enhancement involves directly manipulating the pixels that comprise the image, essentially working on them directly.Conversely, frequency domain enhancement modifies the Fourier transform of the image.
Upon capturing relevant images using digital imaging, subsequent processing becomes imperative.This processing entails compression and coding procedures, wherein all operational data is organized, pertinent data is compressed, and coding is applied.This coding may include model-based coding and neural network coding processing.Following image restoration, transformation occurs, followed by image analysis and subsequent segmentation of relevant images.The regional features of the image are then measured, culminating in the realization of image equipment and presentation.Subsequently, image reconstruction is conducted, followed by image registration.
In the realm of medicine, the pervasive adoption of digital image processing technology has led to the presentation of pertinent diseases through digital images.Utilizing processing technology to manipulate relevant data on-screen, this medical approach significantly enhances the cure rates of associated diseases and achieves more precise treatment outcomes.

Image Restoration Techniques
Image restoration is a valuable process that can significantly enhance the quality of an image, restoring damaged images to their original appearance.Furthermore, this technology can enhance image pixels and eliminate any disharmonious elements present.The crux of image restoration lies in comprehending the factors influencing image quality and devising a scientifically sound operational plan using computer coding technology [8][9].This plan aims to repair processed images or extract initial image information through computer programming, subsequently utilizing image reconstruction to enhance overall image quality.
Moreover, image restoration techniques play a crucial role in various fields such as photography, medical imaging, satellite imaging, and forensic analysis.In photography, image restoration can revitalize old or damaged photographs, preserving precious memories for future generations.Medical imaging heavily relies on image restoration to enhance the clarity and accuracy of diagnostic images, aiding healthcare professionals in making accurate diagnoses and treatment decisions.Satellite imaging benefits from image restoration by improving the resolution and quality of satellite images, enabling better analysis of Earth's surface features and environmental changes.In forensic analysis, image restoration techniques are utilized to enhance surveillance footage or crime scene photographs, assisting investigators in identifying critical details and reconstructing events accurately.
Furthermore, the continuous advancement of image restoration technology has led to the development of innovative algorithms and tools that can address increasingly complex image quality issues.Deep learning techniques, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown remarkable efficacy in restoring images with high fidelity and realism.These advanced algorithms can learn complex patterns and structures from large datasets, enabling them to reconstruct images with unprecedented precision and accuracy.As image restoration technology continues to evolve, it holds the potential to revolutionize various industries and applications, empowering professionals to achieve higher quality results and uncover new insights from visual data.

Image Segmentation Technique
Following the acquisition of relevant digital images, subsequent image processing becomes essential, involving tasks such as compression and coding processing.This entails organizing all operational data, compressing pertinent data, and applying coding methods such as model-based and neural network coding processing.After image restoration, the image undergoes transformation, followed by segmentation of relevant regions through image analysis.Subsequently, the regional features of the image are quantified, culminating in the realization of image equipment and presentation.Following image reconstruction, image registration is conducted.
In the realm of image processing, segmentation serves to alleviate the complexity of image processing to some extent, offering assistance in image analysis, research, and recognition.Presently, digital image technology's development remains incomplete, with only rudimentary region and edge segmentation methods available.A more comprehensive approach to image segmentation has yet to be explored.Nevertheless, even simple image segmentation techniques provide some level of assistance for subsequent image processing endeavors.
Furthermore, image segmentation plays a crucial role in various applications such as medical imaging, object recognition, and autonomous driving systems.In medical imaging, accurate segmentation of anatomical structures from MRI or CT scans is essential for diagnosis and treatment planning.Object recognition systems rely on segmentation to distinguish objects from their background, enabling accurate classification and tracking in computer vision tasks.Similarly, autonomous driving systems use image segmentation to detect and classify objects on the road, aiding in real-time decision-making for vehicle navigation and safety.Despite the existing limitations in segmentation techniques, ongoing research and development efforts aim to advance segmentation methods, leading to more robust and accurate results across various domains.
As digital image technology continues to evolve, the demand for advanced image processing techniques grows, driving innovation in segmentation methods.Researchers are exploring novel approaches such as deep learning-based segmentation models, which leverage convolutional neural networks (CNNs) to achieve superior segmentation accuracy and efficiency.These advanced models can automatically learn and extract features from images, enabling them to produce more precise segmentation results compared to traditional methods.Additionally, efforts are underway to develop hybrid segmentation algorithms that combine the strengths of different techniques to address specific challenges in image segmentation.By pushing the boundaries of segmentation technology, researchers aim to unlock new opportunities for enhancing image analysis, interpretation, and understanding across diverse applications and industries.

Image Reconstruction Technique
During digital image acquisition and processing, various factors contribute to the degradation of image resolution, primarily including blurriness, noise, and distortion.Blurriness can arise from factors such as the sensor's shape and size, optical blur induced by the performance of optical components (e.g., the point spread function PSF), and motion blur resulting from the movement of the captured object.Moreover, with the rapid advancements in CT technology, image reconstruction techniques are progressively evolving.
Among the digital image reconstruction technologies, CT iterative reconstruction stands out as one of the most extensively utilized methods.This technology employs a linear processing approach, enabling noise reduction through mathematical methods in the image space.By iterating through the original data space, artifacts can be minimized, resolution enhanced, and reconstruction accelerated.In medical imaging, X-rays pass through the human body and become attenuated, after which they are measured by instruments.This attenuation process, known as the Radon inverse transformation, restores the profile of the human body from the Radon transform results.MRI, on the other hand, operates on a more complex principle based on the phenomenon of nuclear magnetic resonance.When exposed to a strong magnetic field, hydrogen nuclei split into high and low-energy states.Upon receiving a radio frequency pulse, these nuclei transition between energy states, emitting electromagnetic signals as they return to their original states.Variations in the recovery rates of different atomic nuclei, such as hydrogen atoms in water and fat, produce contrasting signals that facilitate imaging.

Advantages of Image Techniques
The process of digital image processing technology involves converting image signals into digital signals, a task performed by computers, hence also referred to as computer image processing technology.This process encompasses image denoising, enhancement, restoration, and coding.Over time, the reproducibility of digital image processing technology has markedly improved, aided by various external factors and advancements in artificial intelligence technology [10].Digital image processing technology exhibits several distinct application characteristics compared to traditional image processing technology.
Firstly, it offers superior reproducibility.In daily communication, copying, pasting, and processing images may result in blurring and loss of original image information.However, with digital image processing technology, since images undergo digital conversion, the original data can be accurately restored, ensuring faithful reproduction without distortion, even during analog image processing, storage, copying, and transmission operations.
Secondly, digital image technology enables precision processing.With advancements in deep learning-based digital image technology, analog images can be digitized into twodimensional arrays with remarkable accuracy.Technical personnel can achieve high precision by adjusting array parameters during processing, ensuring precise conversion from analog to digital images [11].
Furthermore, digital image processing technology offers flexibility in processing scope.Unlike optical processing, it can perform both linear and non-linear operations, allowing for processing diverse image and graphic information.This flexibility finds application across various fields, from satellite image processing in aviation to intelligent license plate recognition in transportation.
Lastly, digital image processing technology finds wideranging applications.By digitally converting images from different channels into two-dimensional arrays, it enables centralized collection and processing of image information.Integrated into the curriculum system of information technology and computer majors, it addresses interdisciplinary integration needs and enhances the training of computer professionals in knowledge, skills, and quality.With its broad applicability, digital image processing technology significantly contributes to the advancement of communication engineering in today's multifaceted multimedia communication landscape.

Conclusion
Digital image processing technology boasts a remarkably high application rate, offering advantages beyond those of analog image processing technology.This paper delves into the fundamental concepts and principles of digital image processing technology, leveraging the integration of artificial intelligence and deep learning.It elucidates the benefits of digital image processing technology and explores its potential applications in communication engineering.Despite its promising capabilities, digital image processing technology faces challenges that warrant further investigation.While it enhances the efficiency and quality of communication engineering tasks, unresolved technical issues necessitate ongoing exploration in tandem with the continuous advancement of science and technology.
The pivotal role of digital image processing technology in communication engineering and other domains underscores the importance of intensified research efforts to delve deeper into its intricacies and broaden its scope of application.This endeavor aims to elevate the quality and standards of life by harnessing the full potential of digital image processing technology.As technology continues to advance, digital image processing is poised to assume an even greater role in various fields, including communication engineering, medicine, and transportation.However, it is imperative for technicians to prioritize technical innovation and refinement efforts to address existing limitations and propel the rapid evolution of digital image processing technology towards greater heights.