Camera Device Dynamic Scene Blur Compensation
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Solution Overview
Problem
Traditional cameras struggle to capture clear images of dynamic scenes due to blurring caused by moving objects, as they typically do not store preview images to conserve memory, leading to a lack of detail in the final image.
Innovation Solution
A camera device with a photographing system that identifies dynamic objects in preview images, extracts feature information from fuzzy areas, and combines it with the captured image using an image fusion algorithm to generate a clear target image, compensating for motion blur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If preview images are not stored to conserve memory, then memory usage is reduced, but image detail and motion information are lost
Solution Approach 1:
The system performs preliminary action by capturing and analyzing preview images before the actual photographing moment. It identifies dynamic objects and extracts feature information from preview images in advance, so that when the actual photo is taken, the feature information is already available for fusion, eliminating the need to store multiple preview images for later analysis
Solution Approach 2:
The system extracts only the essential feature information (such as motion vectors, edge features, or key pixel data) from the preview images rather than storing the complete images. This extraction process retrieves the critical motion detail information needed for compensation while using minimal memory storage
2Manufacturing precision
If traditional focusing method is used, then the image at optimal focus position is clear, but moving objects appear blurred due to lack of detail
Solution Approach 1:
The system merges the captured image with feature information extracted from preview images through image fusion algorithms. This combination integrates the sharp focus of the captured image with the motion detail information from preview images, producing a final image that is both in focus and free from motion blur
Solution Approach 2:
The system applies different processing quality to different regions of the image. It identifies dynamic object regions and applies feature fusion specifically to these areas, while maintaining the original high-quality focus for static regions. This local quality approach ensures that moving objects receive enhanced detail treatment without compromising the overall image quality
Data Source
AI summary
In capturing images, a method applied in a camera device taking images of a dynamic scenes which provides compensation for blurring and lack of clarity caused by movement of objects within the scene. From images captured in preview mode, determining whether a target object is a dynamic object. A preset number of the preview images are stored as first images of a target object determined as being dynamic, and a second image is also taken, the second image being analyzed for an area of fuzziness. The camera device takes feature information from each first image and the feature information corresponding to the area of fuzziness of the second image and combines feature information having a highest similarity with the fuzzy area to generate a final target image with sharpness and full clarity, and outputting the target image.