Binocular Pan-Tilt Camera Image Reconstruction via Block Fusion
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Solution Overview
Problem
Existing super-resolution algorithms struggle to effectively enhance the definition of short-focus images while preserving the large field-of-view advantage of binocular long-focus and short-focus pan-tilt cameras, often resulting in poor picture quality or errors when significant differences exist between image areas.
Innovation Solution
An image reconstruction method that involves rotating a binocular long-focus and short-focus pan-tilt camera to acquire overlapping long-focus images, downsampling these images to match the resolution of short-focus images, and fusing blocks of the short-focus images with matching high-frequency components from the long-focus images, using a weighted fusion based on similarity to improve the short-focus image definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single-frame super-resolution algorithm is used to enhance short-focus images, then the algorithm complexity increases, but the image enhancement effect is limited
Solution Approach 1:
The patent divides the short-focus image into multiple blocks and processes each block independently by matching with corresponding blocks from long-focus images. This segmentation allows the system to leverage long-focus image details for specific regions while maintaining overall image structure, achieving better enhancement without requiring complex global processing algorithms.
Solution Approach 2:
The patent introduces long-focus images as intermediary reference materials to enhance short-focus images. Instead of directly processing the low-resolution short-focus image through complex algorithms, the system uses high-resolution long-focus images as intermediaries to provide detailed information that can be merged with the short-focus image blocks, simplifying the overall enhancement process.
2Area of stationary object
If dual-focus cameras are used to acquire both wide-angle and long-focus images, then the field-of-view coverage is improved, but the overlapping area limitation reduces enhancement effectiveness in areas with significant differences
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on their characteristics. By dividing the image into blocks and processing each block independently with appropriate long-focus image references, the system adapts the enhancement quality to local requirements, ensuring that areas with significant differences receive targeted attention while maintaining overall field-of-view coverage.
Solution Approach 2:
The patent dynamically selects and matches long-focus images with short-focus image blocks based on their spatial relationships and content similarities. This dynamic matching process allows the system to adapt to varying image characteristics and optimize the enhancement effect for each specific region, overcoming the limitations of fixed overlapping area assumptions.
3Measurement precision
If long-focus image high-resolution information is migrated to short-focus image areas, then the spatial resolution is improved, but reconstruction errors occur when significant differences exist between image parts
Solution Approach 1:
The patent segments both the short-focus and long-focus images into corresponding blocks before processing. This segmentation ensures that only matching blocks with similar content and spatial relationships are combined, preventing reconstruction errors that would occur if entire images were processed as single units. The block-level matching maintains spatial resolution while ensuring accuracy.
Solution Approach 2:
The patent incorporates similarity assessment as a feedback mechanism to evaluate the match quality between short-focus image blocks and long-focus image blocks. By assessing similarity and using this feedback to guide the merging process, the system can identify and avoid mismatched regions, preventing reconstruction errors while maintaining high spatial resolution in accurate matches.
Data Source
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AI summary
Embodiments of the present application provides an image reconstruction method and device. The method comprises: when a long-focus image set acquisition condition is met, driving the pan-tilt platform of a binocular long-focus and short-focus pan-tilt camera to rotate throughout a field-of-view range, obtaining one long-focus image every preset horizontal and/or vertical angle, and constituting a first long-focus image set with all long-focus images acquired throughout the field-of-view range; receiving a zoom request, and performing interpolating on the first short-focus image currently acquired to obtain a second short-focus image that meets the zoom request; downsampling each long-focus image in the first long-focus image set to obtain a second long-focus image set; dividing the second short-focus image into blocks, searching, for each of the divided blocks, all long-focus images in the second long-focus image set for a matching block; fusing each of the divided blocks in the second short-focus image with the matching block for this divided block to obtain a reconstructed short-focus image. The embodiments of the present application not only retain the advantage of a large field-of-view of the short-focus image, but also improves the definition.