Unified Image Restoration Network for Multiple Degradation Factors
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Solution Overview
Problem
Existing machine learning-based image quality improvement models are limited to well-known single degradation cases and struggle to adapt to unpredictable real-world environments and various types of multiple degradation situations, requiring large network parameters and computations.
Innovation Solution
An all-in-one image quality improvement model that integrates multiple degradation factor restoration networks, extracts core network kernels, and performs machine learning to enhance image quality using integrated and single restoration networks, even with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If separate restoration networks are constructed for each degradation factor, then image quality restoration accuracy is improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The patent merges multiple separate restoration networks into a single unified all-in-one restoration network that can handle multiple degradation factors simultaneously. This consolidation reduces device complexity and computational overhead while maintaining restoration accuracy through shared feature extraction layers and a unified restoration module.
Solution Approach 2:
The all-in-one restoration network is designed with universal functionality to handle various degradation factors including blur, noise, rain, snow, and haze within a single model. The network uses a shared backbone that can adapt to different degradation types, eliminating the need for multiple specialized networks.
2Adaptability or versatility
If multiple separate models are used for different degradation factors, then comprehensive coverage of degradation types is improved, but resource consumption increases
Solution Approach 1:
The patent combines multiple degradation-specific models into a single resource-efficient all-in-one model that processes various degradation types using shared computational resources, thereby reducing overall resource consumption while maintaining comprehensive coverage.
Solution Approach 2:
The all-in-one restoration network implements multi-functionality by incorporating degradation type classification and adaptive processing that allows a single model to efficiently handle multiple degradation factors without requiring separate dedicated resources for each type.
3Device complexity
If a single all-in-one model is used for multiple degradation factors, then device complexity is reduced, but image quality restoration accuracy may deteriorate
Solution Approach 1:
The all-in-one restoration network is segmented into distinct functional modules including a degradation type classification module, a shared feature extraction backbone, and degradation-specific restoration branches. This modular segmentation allows the single model to maintain high restoration accuracy for each degradation type while keeping overall device complexity manageable.
Solution Approach 2:
The network implements local quality by having degradation-specific restoration branches that apply specialized processing tailored to each degradation type (blur, noise, rain, snow, haze) while sharing a common backbone, thereby maintaining high restoration accuracy without requiring completely separate models.
4Reliability
If separate restoration networks are trained for each degradation factor, then restoration performance for specific factors is improved, but training time and computational cost increase
Solution Approach 1:
The patent merges the training processes of multiple degradation-specific networks into a single unified training process for the all-in-one model. By training one model on a comprehensive dataset containing multiple degradation types simultaneously, the total training time is significantly reduced compared to training separate models sequentially, while maintaining restoration performance through multi-task learning.
Data Source
AI summary
The present disclosure provides an all-in-one image quality improvement model providing method, which restores quality of images degraded due to multiple image quality degradation factors and is performed by a server, including collecting low-quality images of which image quality is degraded due to at least one of the multiple image quality degradation factors and determining an image quality degradation factor that is a cause of image quality degradation of a corresponding image, constructing an integrated restoration network and a single restoration network to restore the low-quality images with respect to the determined image quality degradation factors.