Unified Image Distortion Removal for Multi-Type Processing
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Solution Overview
Problem
Existing image processing systems require multiple models for different distortion types, necessitating extensive retraining and large data processing, with no guarantee of successful convergence.
Innovation Solution
A unified distortion removal model that can handle various distortion types and degrees using data augmentation and a neural network-based autoencoder, followed by a pre-trained deep learning model for image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple dedicated image processing models are constructed for different distortion types, then each model can be optimized for its specific distortion type, but the system complexity increases and extensive retraining is required for each model
Solution Approach 1:
The patent applies universality by designing a single image processing model that can handle multiple distortion types (radial distortion, tangential distortion, perspective distortion, etc.) through data augmentation. Instead of creating separate dedicated models for each distortion type, the system uses one universal model trained on augmented data representing various distortion scenarios, thereby reducing system complexity while maintaining comprehensive distortion removal capability
Solution Approach 2:
The patent employs parameter changes by introducing distortion parameters (k1, k2, k3 for radial distortion; p1, p2 for tangential distortion) as variable training inputs. The data augmentation process dynamically generates different distortion levels and types by adjusting these parameters, allowing the single model to learn and adapt to various distortion conditions without requiring separate models for each distortion type
2Measurement precision
If multiple dedicated image processing models are constructed for different distortion types, then each distortion type can be handled specifically, but the training time and data processing requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing images through data augmentation to generate diverse training datasets that encompass multiple distortion types and levels before model training. By preparing augmented training data in advance that includes various distortion scenarios, the system enables a single model to be trained comprehensively, avoiding the need for repeated training of multiple dedicated models and significantly reducing total training time
Solution Approach 2:
The patent merges multiple distortion training tasks into a single unified model training process. Instead of training separate models for radial distortion, tangential distortion, and other distortion types, the system combines all distortion types into one training framework using data augmentation, where a single model learns to handle all distortion types simultaneously, thereby consolidating training time and computational resources
3Device complexity
If a single unified distortion removal model is used, then the system complexity is reduced and training is simplified, but the model must handle various distortion types and degrees without specialized optimization
Solution Approach 1:
The patent uses parameter changes to enhance the unified model's adaptability. By incorporating variable distortion parameters (k1, k2, k3 for radial distortion; p1, p2 for tangential distortion) as dynamic training inputs, the single model learns to adapt to different distortion types and severity levels. This parameter-based approach allows the model to generalize across multiple distortion scenarios without requiring specialized sub-models for each distortion type
Solution Approach 2:
The patent applies dynamics by making the training data dynamic and adaptable through data augmentation. The system generates training samples with varying distortion parameters, allowing the unified model to learn flexible adaptation to different distortion conditions. This dynamic training approach enables the single model to handle diverse distortion types effectively, compensating for the lack of specialized optimization through learned generalization
Data Source
AI summary
A method for processing images to remove distortions of various types without requiring retraining for per-type processing models, applied in an electronic device, establishes a distortion removal model, and inputs distorted images in the distortion removal model. General distortions are removed by the distortion removal model and the restored images are input into a deep learning model for final image processing. The deep learning model of the present disclosure finally corrects distortion in the images so that distortion-free images are obtained.