Image Processor Grouping by Blurring Direction for Camera Shake Compensation
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Solution Overview
Problem
Conventional image processing technologies struggle to effectively compensate for camera shake in low-light environments, as they require short-time exposures and emphasize noise in images, leading to suboptimal results.
Innovation Solution
An image processor and associated program that group images by blurring direction, generate composite images for each group, and apply inverse transform filtering to compensate for camera shake, improving noise rejection and visibility without degrading image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If short-time exposure images are used for hand shake compensation, then image sharpness and noise reduction are improved, but insufficient exposure occurs in low-light environments
Solution Approach 1:
The patent segments the hand shake compensation process by grouping images according to their blurring direction components. Instead of treating all images uniformly, it divides them into groups with similar blurring characteristics, allowing targeted processing for each group while maintaining overall compensation effectiveness in low-light conditions
Solution Approach 2:
The patent changes the parameter of exposure time by capturing multiple consecutive images with different exposure durations. By combining these images with varying exposure times and applying selective inverse transform filtering based on blurring direction, it achieves sufficient overall exposure while maintaining sharpness in low-light environments
2Measurement precision
If inverse transform filter is applied to blurred images for hand shake compensation, then position displacement is corrected, but noise components are emphasized
Solution Approach 1:
The patent applies local quality by selectively applying inverse transform filtering only to frequency components corresponding to the detected blurring direction. Instead of uniformly processing all frequency components, it targets only the relevant directions, thereby correcting position displacement while avoiding amplification of noise in other directions
Solution Approach 2:
The patent applies partial action by using selective inverse transform filtering that operates only on specific frequency components related to the blurring direction, rather than applying the filter to the entire image spectrum. This partial application achieves position correction without the excessive noise emphasis that would result from full-spectrum filtering
3Measurement precision
If conventional hand shake compensation is applied to long-time exposure images, then camera shake is corrected, but image quality degrades due to noise
Solution Approach 1:
The patent segments long-time exposure images into groups based on their blurring direction components and applies selective inverse transform filtering to each group. This segmentation allows the system to process images with different exposure times and blurring characteristics separately, achieving effective hand shake compensation while managing noise from long-time exposures
Solution Approach 2:
The patent creates a composite processing approach by combining multiple images with different exposure times and applying different processing strategies to different groups. By synthesizing results from multiple processed images, it achieves superior hand shake compensation and noise management compared to processing single long-time exposure images
Data Source
AI summary
Feature points are extracted from an image of a plurality of frames. A blurring direction component of a previous frame image is judged from motion vectors of the next frame image acquired with reference to feature points of the extracted previous frame image. Images of the same blurring direction are selected and grouped from the image of a plurality of frames based on that judgment result. After performing position compensation, an additive composite of a grouped composite image is created for each group so that feature points conform to grouped images. Subsequently, inverse transform filter processing is applied to the grouped composite image data and hand shake compensation by group is accomplished which compensates the blurring direction component matched to a group. Upon position compensation, an additive composite is generated of a hand shake compensated image so that feature points of each grouped composite image conform to hand shake compensation.