Object-Specific Image Correction Using Neural Network Filters
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Solution Overview
Problem
Existing image correction technologies struggle to effectively address the specific needs of individual objects within an image, leading to suboptimal correction results.
Innovation Solution
A method and device that utilize a neural network to identify objects in an image and determine corresponding correction filters, allowing for personalized image correction based on the attributes of each object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single correction filter is applied to the entire image, then the correction process is simple and fast, but the correction precision for individual objects is poor
Solution Approach 1:
The image is segmented into multiple objects using object detection technology. Each object is identified and separated from the rest of the image, allowing for independent correction filter application. This segmentation enables precise correction for each object while maintaining system feasibility through automated processing.
Solution Approach 2:
Different correction filters are applied to different objects based on their specific attributes and requirements. Each object receives a customized correction filter tailored to its characteristics, achieving local optimization of correction quality rather than uniform correction across the entire image.
2Manufacturing precision
If multiple correction filters are applied to different objects, then the correction precision for each object is improved, but the processing time and computational complexity increase
Solution Approach 1:
Object detection and identification are performed in advance before correction filter application. The system pre-processes the image to identify all objects and determine appropriate correction filters, so that the actual correction process can proceed efficiently without delays during filter selection and application.
Solution Approach 2:
The system automatically detects objects, identifies their attributes, selects appropriate correction filters, and applies them without requiring manual intervention. This self-service approach reduces processing time by eliminating manual object identification and filter selection steps.
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.