Real-Time Video Super-Resolution via Selective Region Processing
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Solution Overview
Problem
Current super-resolution methods for improving image resolution in visual surveillance are computationally expensive, making them unsuitable for real-time applications, especially when dealing with low-resolution CCTV video images of persons, vehicles, or objects at a distance.
Innovation Solution
A system for real-time super-resolution of image regions in video feeds, featuring a fast and accurate approximation technique with interactive graphical user interface modes for selective and dynamic enhancement of target regions, allowing users to focus on specific areas and maintain a gallery of identifiable images, while utilizing sparse code dictionaries trained from low and high-resolution pairs to reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If super-resolution methods are applied to improve image resolution in surveillance video, then image quality and identification capability are improved, but computational cost increases making real-time processing infeasible
Solution Approach 1:
The patent divides the video stream into discrete frames and further segments each frame into super-resolution regions (SRRs) based on detected objects of interest. This segmentation allows the system to apply computationally intensive super-resolution only to specific regions containing persons, vehicles, or objects, rather than processing the entire frame, thereby maintaining real-time performance while improving identification capability in critical areas.
Solution Approach 2:
The system applies super-resolution selectively to specific regions of interest within video frames rather than uniformly across the entire image. By identifying objects such as persons, vehicles, and luggage, and applying enhanced resolution only to their bounding boxes or surrounding regions, the system optimizes computational resources while maximizing identification quality where it matters most for surveillance purposes.
2Measurement precision
If brute-force super-resolution of entire image frames is applied, then complete coverage is achieved, but computational complexity becomes prohibitive for real-time applications
Solution Approach 1:
The patent implements partial action by applying super-resolution only to detected objects and their surrounding regions rather than the entire frame. The system processes a subset of the image data (objects of interest plus margin regions) which is sufficient for identification purposes without the excessive computational burden of full-frame processing, achieving the necessary resolution accuracy with reduced complexity.
3Reliability
If super-resolution is applied to all detected targets in video stream, then identification capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the video processing task by first detecting objects, then creating super-resolution regions around detected targets, and finally applying super-resolution only to these segmented regions. This segmentation strategy maintains identification reliability for all detected targets while reducing overall processing time by excluding non-critical areas from intensive processing.
Data Source
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AI summary
Method and procedures for real-time super-resolving all target image patches defined either by a user specified target region(s) / location specific watchlist (zones) or external signal determined regions of dynamic selection of interest (e.g. human face or body patches, or vehicle number plate image patches) or any selected image patch without object detection in the video stream. Constructing an accurate and very fast deterministic and non-iterative procedure to construct a spare code dictionary of low resolution images of selected regions or the entire video input frames. A cross-platform run-time design procedure for real-time processing without the use of any special dedicated hardware.