Selective Image Deblurring Through Motion-Based Region Analysis
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Solution Overview
Problem
Images captured by cameras can become blurry due to camera or subject movement during exposure, leading to degraded image quality, and existing methods to address this often result in computational inefficiencies or artifacts.
Innovation Solution
A system and method for deblurring images using motion analysis to identify blurry portions, determine the amount of blur, and selectively deblur only those portions, combining them with the rest of the image to improve clarity while conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deblurring methods are applied to entire images, then image clarity is improved, but computational demands increase significantly
Solution Approach 1:
The patent divides the image into multiple regions based on motion analysis, identifying only those regions containing moving objects that require deblurring. This segmentation approach allows the computationally intensive deblurring process to be applied selectively to small portions of the image rather than the entire image, significantly reducing computational demands while maintaining image clarity where needed.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. Regions with moving objects receive full deblurring processing to maximize clarity, while static regions are left unprocessed or receive minimal processing. This local quality approach ensures optimal image clarity in critical areas without unnecessarily consuming computational resources in areas that do not require enhancement.
2Measurement precision
If deblurring is applied to entire images, then blurry portions are corrected, but risk of introducing artifacts increases
Solution Approach 1:
By segmenting the image into moving and static regions, the patent limits deblurring operations to only those segments containing motion. This prevents the deblurring algorithm from processing static regions where it might introduce artifacts such as noise amplification, false edges, or unnatural texture patterns, thereby reducing the overall risk of artifact introduction while still correcting blur in moving objects.
Solution Approach 2:
The patent applies deblurring processing with appropriate quality control only to local regions containing moving objects. By avoiding application of the deblurring algorithm to static regions, the patent minimizes the potential for introducing artifacts such as noise, false structures, or unnatural appearances that can occur when deblurring is applied indiscriminately across the entire image.
3Productivity
If motion analysis is used to identify blurry portions, then processing efficiency is improved, but device complexity increases
Solution Approach 1:
The patent performs motion analysis as a preliminary step before deblurring to identify regions requiring processing. By conducting this analysis first, the system efficiently determines which portions of the image contain moving objects and require deblurring, allowing subsequent processing to be focused only on those identified regions. This preliminary action improves overall processing efficiency by avoiding unnecessary computation on static regions.
Data Source
AI summary
Systems and techniques are described herein for deblurring images. For instance, a method for deblurring images is provided. The method may include identifying, using motion analysis, a portion of an image; identifying an edge associated with the portion; determining an amount of blur of the edge; based on the amount of blur of the edge exceeding a blur threshold, deblurring the portion of the image to generate a deblurred portion of the image; and combining the deblurred portion of the image with other image data to generate a deblurred image.