A real-time video enhancement system using diffusion-based AI models

Abstract

A system (100) for real-time video enhancement using diffusion-based AI models, consisting of: a video input processing module configured to receive and preprocess video images by performing noise reduction, image alignment, and dynamic content analysis; a diffusion-based AI enhancement module configured to apply iterative noise-to-image transformations to refine video images, restore lost details, improve resolution, and correct color distortions; an adaptive parameter tuning module configured to dynamically adjust enhancement parameters based on real-time video characteristics such as motion intensity, lighting conditions, and noise levels; a real-time optimization module configured to utilize model compression, edge computing, and parallel processing to reduce computational complexity and achieve low-latency processing; a motion stabilization module configured to detect and compensate for motion artifacts and flicker effects to ensure smooth video playback; an output / playback module configured to process and reconstruct the enhanced video images for real-time display, storage, or transmission over digital networks.

Description

The present invention relates to the field of artificial intelligence, and more particularly to real time video enhancement techniques. More specifically, it is a system that employs diffusion-based AI models to improve video quality by reducing noise, restoring details, and dynamically improving resolution.Video enhancement is an important technology used in various applications such as surveillance, broadcasting, telemedicine and augmented reality (AR) / virtual reality (VR). The main goal of video enhancement is to improve visual quality by reducing noise, sharpening details, correcting for color distortions, and increasing resolution. However, conventional video enhancement techniques suffer from several limitations that impair their effectiveness, particularly in real-time applications.One of the most common approaches to video enhancement is filter-based techniques such as Gaussian filters, median filters, and bilateral filters. These techniques, while contributing to the reduction of noise, often result in fine detail loss and cause blurring effects, which affect overall quality. Unsharp masking is also frequently used for edge enhancement, but tends to amplify noise and generate artifacts, so that it is not suitable for high-quality video enhancement.Another widely used approach is interpolation-based super resolution, including bicubic, bilinear, and Lanczos interpolation techniques. These techniques scale video images high, but cannot restore lost detail, resulting in artificial textures and lack of clarity in the improved video. This limitation is particularly evident in low resolution videos where the interpolated images are blurred and unnatural.Frequency domain techniques such as wavelet transform and Fourier transform based methods have also been studied for noise suppression and detail improvement. These techniques, while capable of effectively processing video signals in various frequency components, are computationally intensive and can be difficult to perform in real-time processing environments.With advances in artificial intelligence, machine learning techniques and convolutional neural networks (CNN) based on super resolution have gained popularity. Models such as SRCNN and ESPCN attempt to reconstruct high resolution images from low resolution inputs. However, these models require large sets of data for training, making them resource intensive. They also have to combat motion artifacts, especially in fast moving scenes, leading to inconsistencies and flicker effects in the improved video.Generative adventious networks (GANs) have been used for video enhancement and provide improvements in texture recovery and detail generation. However, GAN-based methods tend to produce hallucinated details, leading to distortions and non-realistic visual artifacts. Moreover, they require considerable computing power, which restricts their application in real-time scenarios.To solve this problem, the present invention provides a system for real-time video enhancement using diffusion-based AI models.The real-time video enhancement system with diffusion-based AI models may use diffusion-based AI models to enhance the clarity, sharpness, and general visual quality of videos.The real-time video enhancement system with diffusion-based AI models that can enhance the resolution and details of videos without introducing artifacts, hallucinated features, or excessive noise, thereby ensuring natural and realistic output.The system for real-time video enhancement with diffusion-based AI models, which by optimizing diffusion-based AI models can minimize computational complexity, thereby being suitable for real-time applications without the quality suffering therefrom.The system for real-time video enhancement using diffusion-based AI models, which can reduce motion artifacts and flicker effects that are common in existing AI-based video enhancement techniques, particularly dynamic and motion-intensive scenes.The real-time video enhancement system utilizes diffusion-based AI models that allow efficient noise suppression and denoised, thus ensuring smooth and true-to-original video output, particularly in poor lighting conditions or compressed video.The real-time video enhancement system with diffusion-based AI models that can integrate adaptive processing mechanisms that dynamically adapt the enhancement parameters to the video content, thus ensuring optimal results in different scenes and different lighting conditions.The system for real-time video enhancement with diffusion-based AI models, which may enable seamless deployment in real-world applications such as surveillance systems, streaming services, medical imaging, AR / VR environments, and video transmissions, by ensuring compatibility with existing hardware and software infrastructures.The system for real-time video enhancement uses diffusion-based AI models that can ensure energy efficiency and low latencies, thereby making the system suitable for edge devices, mobile platforms, and real-time streaming applications.In one embodiment, a system for real-time video enhancement with diffusion-based AI models is provided. The system improves video quality by improving resolution, reducing noise, and preserving fine detail. The system includes a video input processing unit, a diffusion-based AI enhancement module, an adaptive parameter tuning system, and an output rendering unit. The video input processing unit extracts images and applies preprocessing techniques such as noise suppression and image alignment. The diffusion-based AI enhancement module applies a controlled noise-to-image transformation process and iteratively refines the images to restore lost detail, correct colors, and improve clarity. In contrast to conventional AI-based super resolution techniques, the proposed system has an adaptive parameter tuning mechanism that dynamically adjusts the enhancement parameters to the real-time video features and thus ensures optimum results at different lighting conditions and motion intensities. The system also includes real-time optimization techniques, including model compression, edge computing deployment, and parallel processing, to achieve low latencies and efficient processing without sacrificing improvement quality. Additionally, motion stabilization algorithms are incorporated to minimize flicker and motion artifacts and to provide smooth video playback. The system can be used in surveillance, broadcasting, telemedicine, ARNR environments and video streaming services where real-time video enhancement is critical. System compatibility with existing hardware and software infrastructures ensures seamless integration into modern video processing pipelines.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows a system for real-time video enhancement with diffusion-based AI models.FIG. 1 shows a system ( 100) for real-time video enhancement with diffusion-based AI models. The system (100) includes a video input processing module, a diffusion-based AI enhancement module, an adaptive parameter adjustment module, a real-time optimization module, a motion stabilization module, and an output rendering module, all interconnected to perform real-time video enhancement. The video input processing module is configured to receive video images and to apply preprocessing techniques such as noise suppression, image alignment, and dynamic content analysis. It uses AI-based noise cancellation algorithms to eliminate compression artifacts and improve visual clarity. The preprocessed frames are then passed to the diffusion-based AI enhancement module, which applies an iterative noise-to-image transformation technique with forward and backward diffusion processes. This module refines video images by restoring lost detail, improving resolution, and correcting for color distortions. The adaptive parameter tuning module dynamically and in real time adjusts the enhancement parameters based on varying video characteristics such as motion intensity, lighting conditions, and noise levels. It uses reinforcement learning based mechanisms to optimize settings to ensure that the diffusion-based AI enhancement module operates at maximum efficiency. To ensure minimum processing delays, the real-time optimization module implements model clipping and quantization techniques. It uses parallel processing and edge computing to achieve low latency video enhancement with consistently high quality. Moreover, this module is integrated with the diffusion-based AI enhancement module to efficiently manage computing resources. The motion stabilization module is responsible for detecting and compensating motion inconsistencies, artifacts, and flicker effects within the video images. By using optical flow-based motion estimation, the module ensures that the improved videos have smooth transitions and stability. It cooperates with the adaptive parameter adjustment module to fine tune the stabilization parameters based on the detected motion patterns. Finally, the output rendering module reconstructs the enhanced video images for real-time display, storage, or transmission over digital networks. It processes the final output in various formats including high definition (HD), 4K, HDR, and real-time streaming resolutions. This module ensures that the system (100) provides optimized video quality while enabling efficient data transmission. The video input processing module, the diffusion-based AI enhancement module, the adaptive parameter tuning module, the real-time optimization module, the motion stabilization module, and the output rendering module are interconnected via a central processing frame that synchronizes the data flow and the computation tasks. The integration of these modules allows the system (100) to provide seamless real-time video enhancement while accommodating dynamic scene changes and ensuring optimal visual quality and reproduction stability.List of reference characters100 System

Claims

A system (100) for real-time video enhancement using diffusion-based AI models, comprising: a video input processing module configured to receive and pre-process video images by performing noise suppression, image alignment, and dynamic content analysis; a diffusion-based AI enhancement module configured to apply iterative noise-to-image transformations to refine video images, restore lost details, improve resolution, and correct color distortions; an adaptive parameter adjustment module configured to dynamically adjust enhancement parameters based on real-time video features such as motion intensity, illumination conditions, and noise levels; a real-time optimization module configured to utilize model compression, edge computing deployment, and parallel processing to reduce computational complexity and achieve low latency processing; a motion stabilization module configured to detect and compensate for motion artifacts and flicker effects to ensure smooth video playback; an output playback module configured to process and reconstruct the enhanced video images for real-time display, storage, or transmission over digital networks.The system (100) of claim 1, wherein the video input processing module is configured to include an AI-controlled noise cancellation algorithm to filter out compression artifacts and improve clarity before the diffusion-based enhancement is applied.The system (100) of claim 1, wherein the diffusion-based AI enhancement module is configured to use a probabilistic generative model that iteratively refines frames using forward and backward diffusion processes.The system (100) of claim 1, wherein the adaptive parameter tuning module is configured to use a reinforcement learning based mechanism to dynamically optimize the enhancement settings based on scene content.The system (100) of claim 1, wherein the real-time optimization module is configured to use model clipping and quantization techniques to ensure computational efficiency while still obtaining improvement quality.The system (100) of claim 1, wherein the motion stabilization module is configured to integrate an optical flow-based motion estimate to mitigate motion inconsistencies and reduce flicker effects.The system (100) of claim 1, wherein the output rendering module is configured to generate enhanced video output in multiple formats including 4K, HDR, and real-time streaming resolutions.The system (100) of claim 1, wherein the system (100) is configured to be deployed on cloud-based or edge computing platforms for scalable real-time video processing.

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