Super-Resolution Image Processing for Security Surveillance Clarity
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Solution Overview
Problem
Traditional digital zoom methods for security surveillance videos lose clarity when amplifying local details, failing to provide a clear display of local details in security scenes.
Innovation Solution
An image processing method that selects a target region in real-time video images, separates static and dynamic images using a matting algorithm, and applies super-resolution processing to enhance the resolution of the target images, allowing for clearer display of local details by superimposing static and dynamic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If traditional digital zoom methods are used to amplify local details in security surveillance videos, then the magnification of local regions is achieved, but the clarity of the displayed details is lost
Solution Approach 1:
The patent segments the video processing into three distinct components: static image extraction, dynamic image extraction, and super-resolution processing. By separating these functions, the system can apply different processing strategies to different parts of the image, maintaining clarity while achieving magnification. The static and dynamic images are processed independently and then combined, resolving the contradiction between magnification and clarity.
Solution Approach 2:
The patent performs preliminary extraction of static and dynamic images from the video frames before applying super-resolution processing. This preliminary segmentation allows the super-resolution algorithm to work on pre-processed components, improving the effectiveness of the magnification process and preserving detail clarity that would be lost in traditional direct digital zoom methods.
2Measurement precision
If super-resolution processing is applied to the entire image, then the resolution of the whole image is improved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent applies super-resolution processing selectively only to the extracted static and dynamic image components rather than the entire video frame. This localized approach maintains high resolution where needed (in the target region of interest) while reducing processing time and computational resources for the rest of the image, effectively resolving the contradiction between image resolution and processing efficiency.
Solution Approach 2:
Instead of applying full super-resolution processing to the entire image, the patent uses partial action by processing only the extracted static and dynamic components. This partial processing approach achieves sufficient resolution improvement for the critical regions while avoiding the excessive computational burden of processing the complete image, balancing quality and efficiency.
Data Source
AI summary
Provided in the embodiments of the present disclosure is an image processing method. The method comprises: acquiring real-time video images; selecting a target area from an image frame, at a selected moment, of the real-time video images; and inputting a first image of the target area into an image processing model to obtain a target image, wherein the resolution of the target image is higher than that of the first image; and providing a first interface, and displaying the target image on the first interface.