AI Image Processing With Example Data for Diverse Image Types
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Solution Overview
Problem
Existing deep learning-based image processing models struggle to process diverse types of images with high performance, requiring individual models for each type and face challenges in identifying the image type beforehand, making it difficult to handle new types of images.
Innovation Solution
An artificial intelligence-based image processing method that inputs both input image data and example image data into a pre-trained model, allowing it to process various types of images reliably without re-training, using techniques like supervised learning and example image data selection based on attributes, resolutions, and processing difficulties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual deep learning models are built for each image type, then processing performance for specific image types is improved, but device complexity and processing time increase
Solution Approach 1:
The patent applies universality by designing a single image processing model that can handle multiple types of images (indoor, outdoor, night scenes, etc.) through a unified architecture. The model uses a universal feature extraction backbone that processes all image types, with task-specific heads that adapt to different processing objectives, eliminating the need for separate models for each image type while maintaining high processing performance.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting model parameters and processing strategies based on the input image characteristics. The system analyzes image attributes (lighting conditions, scene type, quality metrics) and adapts processing parameters accordingly, allowing a single model to optimize its performance for different image types without requiring separate trained models for each category.
2Reliability
If image type classification is performed before processing, then appropriate model selection is improved, but processing time and complexity increase
Solution Approach 1:
The patent applies preliminary action by performing lightweight image attribute analysis at the beginning of the processing pipeline to identify key characteristics (indoor/outdoor, lighting conditions, scene type). This preliminary classification enables the system to select appropriate processing strategies and parameters without requiring time-consuming full model evaluation or multiple sequential classification steps, thus reducing overall processing time while maintaining accurate model selection.
3Manufacturing precision
If multiple processing operations are performed sequentially, then image quality improvement is improved, but processing time increases
Solution Approach 1:
The patent applies merging by integrating multiple image processing operations (denoising, super-resolution, color enhancement, contrast adjustment) into a single unified neural network model. The model processes all these operations in parallel through shared computational layers and coordinated output heads, eliminating the need for sequential processing of separate operations. This combined approach maintains comprehensive image quality improvement while significantly reducing processing time by avoiding multiple separate inference passes.
Data Source
AI summary
Provided is an artificial intelligence-based image processing method includes a method performed by a computing device and including acquiring input image data to be processed, acquiring example image data corresponding to the acquired input image data, and generating processed image data corresponding to the acquired input image data by inputting the acquired input image data and the acquired example image data into a pre-trained image processing model.