Neural Network Object Detection for Image Correction Filters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image correction methods lack the ability to accurately identify and correct specific objects within an image using advanced AI algorithms, leading to suboptimal image quality and user intention mismatch.
Innovation Solution
A device and method utilizing a neural network to identify objects in an image and determine corresponding correction filters, applying AI algorithms for precise object recognition and filter application, enhancing image quality by matching user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image correction methods are used, then the correction process is simple and fast, but the ability to accurately identify and correct specific objects is insufficient
Solution Approach 1:
The patent segments the image into multiple objects using object detection technology, then applies different correction filters to each object individually. This segmentation approach enables precise identification and correction of specific objects while maintaining system feasibility through modular processing architecture.
Solution Approach 2:
The patent introduces neural networks as an intermediary component between the image input and correction filter application. The neural network automatically identifies objects and determines appropriate correction filters, resolving the contradiction by adding intelligence without requiring complex manual configuration.
2Adaptability or versatility
If uniform correction filters are applied to the entire image, then the correction process is simple, but the correction effect does not match user intentions for specific objects
Solution Approach 1:
The patent applies different correction filters to different objects based on their local characteristics and the user's intended expression effect. Each object receives a customized correction filter determined by the neural network, achieving local optimization that matches user intentions while maintaining overall system coherence.
Solution Approach 2:
The patent changes the parameters of correction filters based on the identified objects and their characteristics. The neural network adjusts filter parameters dynamically to achieve the desired expression effect for each object, enabling adaptable correction without requiring complex manual parameter tuning.
3Manufacturing precision
If multiple objects are corrected individually with tailored filters, then the image quality and user intention alignment improve, but the processing time and computational resources increase
Solution Approach 1:
The patent performs object identification and correction filter determination simultaneously through the neural network in a preliminary processing stage. By identifying objects and determining appropriate filters in one integrated operation rather than sequential steps, the system achieves precise individual correction while minimizing processing time.
Solution Approach 2:
The patent merges the object identification function and correction filter determination function into a single neural network processing operation. This consolidation enables simultaneous execution of multiple tasks, achieving high correction precision for multiple objects without proportionally increasing processing time.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
Provided is a device for modifying an image including a memory storing one or more instructions; and at least one 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 comprising 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 filters respectively corresponding to the identified plurality of objects, and apply the determined plurality of filters respectively to each of the plurality of objects in the image.