Auto Focusing Value Comparison for Shaken Image Detection
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Solution Overview
Problem
Existing digital cameras lack an efficient method to distinguish and delete shaken images, which are often captured due to hand tremble, subject movement, or camera motion, making it difficult for users to select and remove low-quality images from large collections.
Innovation Solution
A method and apparatus using auto focusing (AF) to determine whether an image is shaken by calculating and comparing AF values from a preview image and a captured image, with the difference in AF values determining the image quality, and allowing users to delete or categorize shaken images accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually select and delete shaken images from large collections, then image quality can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary classification of images into shaken and non-shaken categories using AF value comparison during or after capture. By pre-identifying shaken images through automated analysis of focus metrics, the system eliminates the need for users to manually examine and select each image for deletion, thereby maintaining image quality while dramatically reducing time consumption.
2Productivity
If automated methods are introduced to identify shaken images, then time efficiency improves, but system complexity increases
Solution Approach 1:
The system leverages the existing auto-focusing mechanism and AF evaluation values already present in digital cameras for their primary function of focus adjustment. By repurposing these existing components to also perform shaken image detection through comparison of AF values between sequential images, the system achieves automated identification without adding separate complex detection hardware or algorithms.
Solution Approach 2:
The system detects shaken images by monitoring changes in AF evaluation values between consecutive images. When the difference in AF values exceeds a predetermined threshold, the image is classified as shaken. This parameter-based approach uses simple numerical comparison rather than complex image analysis, maintaining low system complexity while achieving high processing efficiency.
3Measurement precision
If AF value comparison is used to detect shaken images, then detection accuracy improves, but computational load increases
Solution Approach 1:
The system extracts only the essential AF evaluation values from the image processing data for shaken image detection, rather than analyzing the entire image content. By isolating and comparing these specific focus metrics between sequential images, the system achieves accurate shaken image detection with minimal computational energy, as AF values are already calculated during normal auto-focusing operations.
Data Source
AI summary
A method and apparatus for determining a shaken image by using auto focusing. The method includes calculating a maximum AF value of a preview image and a maximum AF value of a captured image that is down-sampled according to the preview image and comparing the maximum AF value of the preview with the maximum AF value of the captured image to determine whether the captured image is shaken or not.