Region-Specific Image Restoration for Surveillance Object Clarity
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Solution Overview
Problem
Conventional image restoration methods struggle to effectively restore objects with different attributes in surveillance images due to the use of uniform restoration parameters, leading to suboptimal image quality.
Innovation Solution
The method employs an image restoration device with an image receiver and operation processor to analyze and process images by identifying regions of interest and background regions, applying distinct restoration functions and weights to enhance clarity while maintaining efficient data size and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform restoration parameters are applied to the entire distorted image, then the processing complexity is reduced, but the restoration quality of objects with different attributes deteriorates
Solution Approach 1:
The patent divides the distorted image into multiple regions (first region and second region) with different attributes. The processor applies different restoration functions to each region: a first restoration function for the first region and a second restoration function for the second region. This segmentation allows each region to be restored with parameters optimized for its specific characteristics, improving overall restoration quality without requiring excessive complexity in the entire system.
Solution Approach 2:
The patent implements local quality by applying different restoration parameters to different spatial regions of the image. The first restoration function uses parameters optimized for objects in the first region, while the second restoration function uses parameters optimized for objects in the second region. This local adaptation ensures that each region receives the appropriate restoration treatment for its specific content characteristics.
2Manufacturing precision
If different restoration functions are applied to different regions, then the restoration quality is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the image into distinct regions and applies different restoration functions to each segment. This approach improves restoration quality by tailoring parameters to regional characteristics while managing complexity through structured division rather than requiring complex adaptive algorithms for the entire image.
Solution Approach 2:
The patent applies restoration processing selectively to different regions rather than uniformly to the entire image. By focusing computational resources on applying appropriate restoration functions only where needed for each region, the system achieves high restoration quality while avoiding the excessive complexity that would result from applying complex adaptive algorithms globally.
Data Source
AI summary
An image restoration method is applied to an image restoration device and includes acquiring an analyzing image containing a region of interest and a background region, utilizing a first restoration function to calibrate the region of interest and generate a calibrated region of interest, and combining the calibrated region of interest with the background region via a relative position between the region of interest and the background region for providing a restoration image.