SYSTEM AND METHOD FOR ENHANCING IMAGE QUALITY THROUGH AI PROCESSING

Abstract

ABSTRACT System and Method for Enhancing Image Quality through AI Processing The present disclosure introduces a system and method for enhancing image quality through AI processing 100, which utilizes an adaptive machine learning model 106 to dynamically adjust parameters based on image characteristics, improving resolution, noise reduction, and color accuracy. The system comprises of data acquisition module 102 to gather diverse images, and a preprocessing unit 104 for normalization and noise reduction. A convolutional neural network 108 extracts spatial features, while a generative adversarial network 110 refines image clarity. An integrated noise learning mechanism 112 targets specific noise patterns, and an image enhancement processor 114 applies final adjustments. The post-processing module 116 performs sharpening and compression, while the automated quality assessment module 118 evaluates image quality and provides feedback. Additional components are user interface customization 122, cross-domain generalization mechanism 124, context-aware enhancement system 126, multi-modal input support system 128, and cross-platform deployment framework 138. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00023] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00024] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system and method for enhancing image quality through AI processing and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00025] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00026] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00027] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00028] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00029] Referring to Fig. 1, system and method for enhancing image quality through AI processing 100 is disclosed in accordance with one embodiment of the present invention. It comprises of data acquisition module 102, preprocessing unit 104, adaptive machine learning model 106, convolutional neural network 108, generative adversarial network 110, integrated noise learning mechanism 112, image enhancement processor 114, post-processing module 116, automated quality assessment module 118, performance optimization system 120, user interface for customization 122, cross-domain generalization mechanism 124, context-aware enhancement system 126, multi-modal input support system 128, user feedback loop mechanism 130, energy-efficient processing system 132, batch processing capability 134, AI-driven metadata integration 136, cross-platform deployment framework 138.

[00030] Referring to Fig. 1, the present disclosure provides details of a system and method for enhancing image quality through AI processing 100. This invention is a comprehensive framework that leverages adaptive machine learning, advanced neural networks, and optimized processing techniques to improve resolution, reduce noise, and enhance color accuracy across diverse imaging applications. The system comprises essential components such as data acquisition module 102, preprocessing unit 104, adaptive machine learning model 106, convolutional neural network 108, and generative adversarial network 110 for refined image quality enhancement. Additionally, it features integrated noise learning mechanism 112 and image enhancement processor 114 for achieving clarity and detail. The automated quality assessment module 118 and user interface for customization 122 enable real-time feedback and user control. Other components like cross-domain generalization mechanism 124 and cross-platform deployment framework 138 ensure versatility and scalability across multiple devices and environments.

[00031] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with data acquisition module 102, which gathers diverse images from sources such as photographs, videos, and medical scans. It captures inputs of varying resolutions and quality levels, ensuring a robust dataset for the preprocessing unit 104 and adaptive machine learning model 106. The data acquisition module 102 works closely with preprocessing unit 104 to standardize input data, creating a solid foundation for effective image enhancement.

[00032] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with preprocessing unit 104, which normalizes pixel values, augments data through transformations, and reduces noise in initial inputs. This unit prepares the images, allowing the adaptive machine learning model 106 to focus on feature learning without interference from inconsistent data. The preprocessing unit 104 collaborates directly with the integrated noise learning mechanism 112 to manage noise, creating cleaner data for subsequent image processing.

[00033] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with adaptive machine learning model 106, which dynamically adjusts based on image characteristics. This model refines its parameters continuously to improve output quality, adapting effectively to diverse input images from data acquisition module 102. It coordinates with both convolutional neural network 108 and generative adversarial network 110 to leverage their strengths for optimal image resolution and noise reduction.

[00034] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with convolutional neural network 108, which identifies spatial hierarchies in images, enabling precise resolution enhancement. This network, trained on high-quality image patterns, interworks with adaptive machine learning model 106 to apply learned structures to improve detail and depth in output images. It also supports generative adversarial network 110 by supplying high-resolution features during adversarial training.

[00035] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with generative adversarial network 110, which performs adversarial training by generating high-resolution images from low-quality inputs. The generator and discriminator networks in GAN refine output quality through iterative enhancement, supported by convolutional neural network 108's extracted features. This component is essential for restoring missing details and creating realistic, high-quality images.

[00036] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with integrated noise learning mechanism 112, designed to learn and adapt to noise patterns in input images. It collaborates with preprocessing unit 104 to filter noise in the dataset and further refines noise reduction through the image enhancement processor 114. This mechanism allows selective noise targeting, improving the clarity of processed images.

[00037] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with image enhancement processor 114, which applies trained models to produce enhanced images. This processor integrates outputs from adaptive machine learning model 106, convolutional neural network 108, and generative adversarial network 110 for super-resolution, noise reduction, and color optimization. It performs the core enhancement tasks, delivering high-quality images for diverse applications.

[00038] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with post-processing module 116, which adds final enhancements such as edge sharpening and compression. Working with image enhancement processor 114, this module ensures the final output is visually optimized and suitable for storage or transmission. It applies selective adjustments to enhance specific areas of an image, such as focal points, without affecting overall quality.

[00039] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with automated quality assessment module 118, which evaluates enhanced images against predefined metrics. This module provides real-time feedback to the image enhancement processor 114 and adaptive machine learning model 106 to iteratively improve enhancement quality. It ensures consistent output standards, allowing for fine-tuning in ongoing model adjustments.

[00040] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with performance optimization system 120, designed to streamline processing speed and efficiency through model pruning, quantization, and transfer learning. This system works with adaptive machine learning model 106 to reduce computational load, enabling real-time image enhancement suitable for edge devices and mobile platforms.

[00041] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with user interface for customization 122, allowing users to adjust enhancement settings according to specific requirements. The interface interacts with adaptive machine learning model 106 and image enhancement processor 114 to modify parameters like resolution and noise reduction levels, making the system flexible for various use cases.

[00042] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with cross-domain generalization mechanism 124, enabling the model to adapt and enhance images from diverse sources without extensive retraining. This mechanism utilizes transfer learning to prepare adaptive machine learning model 106 for varied datasets, enhancing the system's versatility in handling different types of input images.

[00043] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with context-aware enhancement system 126, which analyzes the content and context of an image to apply targeted enhancements. This system interacts with image enhancement processor 114 to customize enhancement techniques based on the image type, such as landscapes or portraits, for tailored quality improvements.

[00044] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with multi-modal input support system 128, which integrates data from various imaging sources, such as RGB, thermal, and infrared. This system 128 expands the utility of data acquisition module 102 by accommodating diverse input types and enables image enhancement processor 114 to create more comprehensive and detailed outputs.

[00045] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with user feedback loop mechanism 130, which captures user feedback on image quality and refinement needs. This mechanism 130 works with adaptive machine learning model 106 to iteratively improve model performance based on real-world use and feedback, fostering continuous adaptation and improvement.

[00046] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with energy-efficient processing system 132, optimized to reduce power consumption during processing. This system 132 enables performance optimization system 120 to operate in low-power environments, making the invention suitable for mobile devices and battery-powered applications.

[00047] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with batch processing capability 134, allowing simultaneous enhancement of multiple images. This component 134 enhances system efficiency, enabling image enhancement processor 114 to handle large-scale applications, such as media production or medical imaging, with optimized processing time.

[00048] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with AI-driven metadata integration 136, which enriches images with contextual data like date, location, and device type. This component works with data acquisition module 102 to add valuable insights to enhanced images, assisting users in understanding the enhancement's context and background.

[00049] Referring to Fig. 1, the system and method for enhancing image quality through AI processing 100 is provided with cross-platform deployment framework 138, which ensures compatibility with various platforms, including cloud, edge, and mobile environments. This framework 138 collaborates with performance optimization system 120 to allow scalable deployment across different devices, broadening the system's accessibility and application scope.

[00050] Referring to Fig 2, there is illustrated method 200 for system and method for enhancing image quality through AI processing 100. The method comprises:
At step 202, method 200 includes the data acquisition module 102 collecting images from various sources, such as photos, videos, and medical imaging scans, ensuring a diverse and comprehensive dataset is available for enhancement;
At step 204, method 200 includes the pre-processing unit 104 receiving the acquired images and performing normalization, data augmentation, and initial noise reduction to create a clean, standardized input for analysis;
At step 206, method 200 includes the adaptive machine learning model 106 taking the preprocessed images and dynamically adjusting enhancement parameters based on the specific characteristics of each image, preparing the data for high-resolution transformation;
At step 208, method 200 includes the convolutional neural network 108 receiving the adapted image data from the adaptive machine learning model 106 and extracting detailed spatial hierarchies and features, setting a foundation for high-quality enhancement;
At step 210, method 200 includes the generative adversarial network 110 utilizing the features extracted by convolutional neural network 108 and refining the image further through adversarial training, where the generator and discriminator networks work to enhance image clarity and resolution;
At step 212, method 200 includes the integrated noise learning mechanism 112 analyzing noise patterns identified by the generative adversarial network 110 and selectively targeting and reducing these noise artifacts, enhancing image clarity for the next stage;
At step 214, method 200 includes the image enhancement processor 114 integrating the refined details from the convolutional neural network 108 and the noise-reduced images from the integrated noise learning mechanism 112 to apply the final enhancements in resolution, color accuracy, and sharpness;
At step 216, method 200 includes the post-processing module 116 receiving the fully enhanced images from the image enhancement processor 114 and performing final touches, such as edge sharpening and optimized compression, to ensure the images are suitable for storage or transmission;
At step 218, method 200 includes the automated quality assessment module 118 evaluating the output images from the post-processing module 116 against quality standards, providing feedback to the adaptive machine learning model 106 to improve the model's future performance based on quality results;
At step 220, method 200 includes the user interface for customization 122 enabling users to provide specific enhancement preferences, adjusting parameters in the adaptive machine learning model 106 to achieve personalized image quality outcomes;
At step 222, method 200 includes the cross-domain generalization mechanism 124 using feedback from the quality assessment and customization interface to apply transfer learning, ensuring that the model adapts to varied image types and sources without extensive retraining;
At step 224, method 200 includes the context-aware enhancement system 126 analysing the content type of each image (e.g., landscapes or portraits) and instructing the image enhancement processor 114 to apply content-specific adjustments for optimized results;
At step 226, method 200 includes the multi-modal input support system 128 integrating any additional data from thermal, infrared, or other imaging sources, enriching the standard image data to enhance the output quality with additional details;
At step 228, method 200 includes the user feedback loop mechanism 130 collecting feedback from users on the final image quality, feeding this data back into the adaptive machine learning model 106 to further refine its performance and adaptability in future enhancements;
At step 230, method 200 includes the energy-efficient processing system 132 optimizing processing requirements during all enhancement stages, ensuring that the system can operate efficiently on mobile or battery-powered devices without compromising performance;
At step 232, method 200 includes the batch processing capability 134 processing multiple images in parallel, increasing efficiency and enabling large-scale enhancements for applications like media production and medical imaging;
At step 234, method 200 includes the ai-driven metadata integration 136 attaching contextual data such as date, location, and device type to the enhanced images, offering additional insights for users as part of the final output;
At step 236, method 200 includes the cross-platform deployment framework 138 ensuring the enhanced images and all processing functionalities are compatible across various platforms, including cloud, edge, and mobile environments, enabling seamless usage across different devices and applications.
[00051] The invention's applications span multiple industries in different embodiments where high-quality image processing is critical, including healthcare, digital media, security, and remote sensing. In healthcare, the system can significantly enhance medical imaging, such as MRI and CT scans, by improving resolution and clarity, aiding in more accurate diagnoses and treatment planning. In digital media, it enables photographers and videographers to upscale and refine images and videos, enhancing visual content quality without loss of detail. For security and surveillance, the system improves low-resolution or noisy footage, enabling clearer identification and analysis. Additionally, in remote sensing and environmental monitoring, the invention processes satellite and aerial images, bringing out finer details essential for urban planning, agriculture, and disaster management. The system's ability to adapt across various image types and modalities makes it versatile for any application requiring enhanced image resolution, clarity, and color fidelity, making it invaluable for real-time, resource-constrained environments such as mobile and edge computing.

[00052] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00053] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00054] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A system and method for enhancing image quality through AI processing 100 comprising of
data acquisition module 102 to collect images from diverse sources for a comprehensive dataset;
preprocessing unit 104 to normalize, augment, and reduce noise in image data;
adaptive machine learning model 106 to dynamically adjust enhancement parameters based on image characteristics;
convolutional neural network 108 to extract spatial features for high-resolution output; generative adversarial network 110 to refine images through adversarial training for enhanced clarity;
integrated noise learning mechanism 112 to identify and selectively reduce noise patterns in images;
image enhancement processor 114 to apply final improvements in resolution, color accuracy, and sharpness; post-processing module 116 to refine enhanced images with edge sharpening and compression;
automated quality assessment module 118 to evaluate enhanced images against quality standards;
performance optimization system 120 to streamline processing speed with pruning and quantization;
user interface for customization 122 to allow users to adjust enhancement settings based on needs;
cross-domain generalization mechanism 124 to adapt the model across varied image types and sources;
context-aware enhancement system 126 to apply specialized enhancements based on image content;
multi-modal input support system 128 to integrate data from different imaging sources for enhanced quality;
user feedback loop mechanism 130 to collect user feedback and refine model performance;
energy-efficient processing system 132 to reduce power consumption during image enhancement;
batch processing capability 134 to process multiple images in parallel for efficiency;
AI-driven metadata integration 136 to add contextual data like date and location to enhanced images; and
cross-platform deployment framework 138 to ensure compatibility across cloud, edge, and mobile platforms.
2. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein adaptive machine learning model dynamically adjusts image enhancement settings based on individual image characteristics, optimizing clarity and resolution in real-time.

3. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the convolutional neural network 108 extracts essential spatial features from images, establishing a high-resolution foundation that improves clarity and detail.

4. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the generative adversarial network 110 uses adversarial training to refine images, working with the convolutional neural network to reduce noise and enhance resolution.

5. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the integrated noise learning mechanism 112 detects and reduces specific noise patterns, increasing image clarity by adapting to varying noise levels.

6. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the image enhancement processor 114 is configured to integrate outputs from the convolutional neural network 108 and generative adversarial network 110, applying final adjustments for resolution, color accuracy, and sharpness to produce optimized images.

7. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the cross-domain generalization mechanism 124 adapts the model for different image types and sources, using transfer learning to ensure consistent quality without extensive retraining.

8. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the user interface for customization 122 is configured to allow users to adjust image enhancement parameters, offering tailored control over output characteristics such as resolution, noise reduction, and color balance based on individual needs.

9. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein the automated quality assessment module 118 evaluates enhanced images against quality metrics, providing feedback to continuously improve model performance.

10. The system and method for enhancing image quality through AI processing 100 as claimed in claim 1, wherein method comprises of
data acquisition module 102 collecting images from various sources, such as photos, videos, and medical imaging scans, ensuring a diverse and comprehensive dataset is available for enhancement;
preprocessing unit 104 receiving the acquired images and performing normalization, data augmentation, and initial noise reduction to create a clean, standardized input for analysis;
adaptive machine learning model 106 taking the preprocessed images and dynamically adjusting enhancement parameters based on the specific characteristics of each image, preparing the data for high-resolution transformation;
convolutional neural network 108 receiving the adapted image data from the adaptive machine learning model 106 and extracting detailed spatial hierarchies and features, setting a foundation for high-quality enhancement;
generative adversarial network 110 utilizing the features extracted by convolutional neural network 108 and refining the image further through adversarial training, where the generator and discriminator networks work to enhance image clarity and resolution;
integrated noise learning mechanism 112 analyzing noise patterns identified by the generative adversarial network 110 and selectively targeting and reducing these noise artifacts, enhancing image clarity for the next stage;
image enhancement processor 114 integrating the refined details from the convolutional neural network 108 and the noise-reduced images from the integrated noise learning mechanism 112 to apply the final enhancements in resolution, color accuracy, and sharpness;
post-processing module 116 receiving the fully enhanced images from the image enhancement processor 114 and performing final touches, such as edge sharpening and optimized compression, to ensure the images are suitable for storage or transmission;
automated quality assessment module 118 evaluating the output images from the post-processing module 116 against quality standards, providing feedback to the adaptive machine learning model 106 to improve the model's future performance based on quality results;
user interface for customization 122 enabling users to provide specific enhancement preferences, adjusting parameters in the adaptive machine learning model 106 to achieve personalized image quality outcomes;
cross-domain generalization mechanism 124 using feedback from the quality assessment and customization interface to apply transfer learning, ensuring that the model adapts to varied image types and sources without extensive retraining;
context-aware enhancement system 126 analyzing the content type of each image (e.g., landscapes or portraits) and instructing the image enhancement processor 114 to apply content-specific adjustments for optimized results;
multi-modal input support system 128 integrating any additional data from thermal, infrared, or other imaging sources, enriching the standard image data to enhance the output quality with additional details;
user feedback loop mechanism 130 collecting feedback from users on the final image quality, feeding this data back into the adaptive machine learning model 106 to further refine its performance and adaptability in future enhancements;
energy-efficient processing system 132 optimizing processing requirements during all enhancement stages, ensuring that the system can operate efficiently on mobile or battery-powered devices without compromising performance;
batch processing capability 134 processing multiple images in parallel, increasing efficiency and enabling large-scale enhancements for applications like media production and medical imaging;
AI-driven metadata integration 136 attaching contextual data such as date, location, and device type to the enhanced images, offering additional insights for users as part of the final output;
cross-platform deployment framework 138 ensuring the enhanced images and all processing functionalities are compatible across various platforms, including cloud, edge, and mobile environments, enabling seamless usage across different devices and applications.

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