Object-Specific Image Correction Using Neural Network Filters
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Solution Overview
Problem
Current image correction methods lack the ability to accurately and efficiently apply specific correction filters to individual objects within an image, leading to suboptimal image quality and user intention mismatch.
Innovation Solution
A device and method utilizing a neural network to identify and apply correction filters specifically to each object in an image, based on analyzed image and display attributes, enabling precise correction and improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single correction filter is applied to the entire image, then the processing is simple and fast, but the image quality and user intention matching are suboptimal
Solution Approach 1:
The image is segmented into multiple objects using object recognition technology. Each object is then processed with its own dedicated correction filter based on its specific attributes, rather than applying a single filter to the entire image. This segmentation enables precise, object-specific correction while maintaining processing efficiency through automated attribute-based filter selection.
Solution Approach 2:
Different correction filters are applied to different objects within the image based on their specific attributes. Each object receives a tailored correction filter that matches its characteristics, achieving local optimization of image quality rather than uniform processing. This allows each region to be corrected according to its specific needs.
2Manufacturing precision
If multiple correction filters are applied to different objects, then the image quality improves, but the processing time and computational resources increase
Solution Approach 1:
Object attributes are recognized and analyzed in advance using object recognition technology before correction filter application. This preliminary action of identifying and categorizing objects by their attributes enables the system to pre-determine which correction filters to apply, streamlining the overall processing pipeline and reducing total processing time despite multiple filters being used.
Solution Approach 2:
The system dynamically selects correction filters based on recognized object attributes, changing the processing parameters (filter selection) according to each object's characteristics. This parameter-based approach allows automated, efficient filter selection that reduces manual intervention and optimizes processing speed while maintaining high correction precision.
Data Source
AI summary
An example device for correcting an image includes a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor, by executing the one or more instructions, is further configured to obtain an image including a plurality of objects, identify the plurality of objects in the image based on a result of using one or more neural networks, determine a plurality of correction filters respectively corresponding to the plurality of identified objects, and correct the plurality of objects in the image, respectively, by using the plurality of determined correction filters.