Text-Guided Image Restoration for Noise, Blur, and Occlusions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image restoration methods, such as Gaussian filtering, require manual design of filters for different restoration problems, leading to low efficiency and poor performance for images with varying noise types and intensities.
Innovation Solution
An image restoration method that inputs a to-be-restored image, image description information, and restoration type into an image restoration model, where the model is trained on sub-restoration models corresponding to different restoration types, allowing for accurate and efficient restoration by identifying areas to be optimized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual filter design is used for different restoration problems, then restoration can be performed, but restoration efficiency is low
Solution Approach 1:
The patent divides image restoration into multiple specialized sub-restoration models, each trained for specific restoration types (denoising, deblurring, inpainting). This segmentation allows the system to select and apply only the necessary model for each specific restoration task, improving efficiency by avoiding manual filter design while maintaining specialized restoration capabilities.
Solution Approach 2:
The patent transforms restoration from a manual parameter-adjustment process to an automated deep learning process. By changing the approach from manual filter parameter design to trained neural network models, the system automatically adapts to different restoration problems without manual intervention, significantly improving restoration efficiency.
2Reliability
If Gaussian filter-based method is used, then restoration can be performed, but performance is poor for images with different noise types and intensities
Solution Approach 1:
The patent creates multiple specialized sub-restoration models, each optimized for specific restoration types such as denoising, deblurring, and inpainting. This segmentation enables the system to adapt to different noise types and image degradation patterns by selecting the appropriate specialized model, significantly improving both reliability and adaptability compared to a single Gaussian filter approach.
Solution Approach 2:
The patent develops a universal image restoration model that can handle multiple restoration types through a unified framework. The model incorporates text information and restoration type as inputs, enabling it to adaptively perform various restoration tasks (denoising, deblurring, inpainting) with a single system, improving both versatility and performance across different image degradation scenarios.
3Measurement precision
If a single restoration model is used, then the system is simple, but it cannot accurately handle diverse restoration types
Solution Approach 1:
The patent employs multiple specialized sub-restoration models for different restoration types (denoising, deblurring, inpainting) rather than a single general model. Each sub-model is trained specifically for its restoration type, achieving high accuracy. The system manages complexity by organizing these specialized models in a modular architecture with a unified input/output interface.
Solution Approach 2:
The patent introduces text information and restoration type classification as intermediaries between the input image and the restoration process. This intermediary layer enables the system to accurately identify the restoration type and select or configure the appropriate sub-model, achieving high restoration accuracy while maintaining manageable system complexity through structured information flow.
Data Source
AI summary
The disclose provides an image restoration method, device and apparatus, and relates to the field of image processing technology. The method includes obtaining a to-be-restored image, target text information corresponding to the to-be-restored image, and target restoration type(s), where the target text information is used to describe the to-be-restored image; inputting the to-be-restored image, the target text information and the target restoration type(s) into an image restoration model for image restoration processing, to obtain a restored target image corresponding to the to-be-restored image, where the image restoration model is obtained by training sub-restoration models corresponding to the different restoration types.