Static Scene Image Splitting via Motion and Brightness Correction
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Solution Overview
Problem
Existing image split methods, particularly area growth techniques, face challenges in accurately splitting static scenes due to over-splitting, noise sensitivity, and difficulty in setting precise criteria for intensity changes, leading to deterioration in image split performance.
Innovation Solution
An apparatus and method that utilize multiple image frames to correct movement and brightness components, generating an average image and repeatedly updating it based on variation values until a predetermined threshold is reached, allowing for accurate splitting of edge and non-edge areas using a gradient magnitude threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If area growth technique is used for image split, then implementation simplicity is improved, but noise sensitivity increases and split precision deteriorates
Solution Approach 1:
The patent segments the image processing task into three distinct modules: movement correction module, brightness correction module, and image split module. This segmentation allows each module to specialize in a specific function, improving overall precision while maintaining implementation clarity through modular design.
Solution Approach 2:
The patent applies preliminary actions by performing movement correction and brightness correction before the actual image split operation. The movement correction module aligns multiple frames in advance, and the brightness correction module generates a corrected average image beforehand, ensuring that the subsequent split operation works on pre-processed data with reduced noise and artifacts.
2Object-affected harmful factors
If smoothing operation is applied to reduce noise, then noise level is reduced, but sharp shapes are lost and image split performance deteriorates
Solution Approach 1:
The patent extracts the noise reduction function from traditional smoothing operations and implements it through statistical processing of multiple image frames. By taking out the harmful noise components through averaging while preserving edge information through gradient-based processing, the system achieves noise reduction without losing sharp shapes.
Solution Approach 2:
The patent creates a composite processing approach by combining movement correction, brightness correction, and gradient-based split processing. This composite method integrates multiple processing techniques to achieve both noise reduction and shape preservation, overcoming the limitations of individual methods.
3Object-affected harmful factors
If multiple image frames are processed to generate average image, then noise reduction is achieved, but processing time increases
Solution Approach 1:
The patent performs preliminary movement correction and brightness correction on multiple image frames before the actual split operation. By pre-processing the frames to correct movement and brightness variations, the system reduces the need for iterative adjustments during splitting, thereby reducing overall processing time despite handling multiple frames.
Solution Approach 2:
The patent skips unnecessary processing steps by directly using gradient magnitude calculation on the brightness-corrected average image for split criterion determination. This approach rushes through intermediate processing stages that would otherwise be needed, reducing processing time while maintaining accuracy.
Data Source
AI summary
The present concept relates to an apparatus for dividing of static scene based on statistics of images that enables more accurate image split processing by correcting movement and brightness components based on statistical information of multiple image frames for a static scene to generate an average image and by splitting the edge area and the non-edge area from the average image, and a method thereof.