Text-Guided Image Restoration for Multiple Restoration Types
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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 trained with sub-restoration models corresponding to different restoration types, allowing for accurate and efficient restoration by guiding the model's attention to specific areas using target text information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual filter design is used for different restoration problems, then restoration accuracy can be maintained for specific problem types, but restoration efficiency deteriorates significantly
Solution Approach 1:
The patent segments the restoration task by dividing the image into multiple patches and processing each patch independently through the trained model. This allows parallel processing of multiple regions simultaneously, significantly improving efficiency while maintaining accuracy through localized restoration decisions
Solution Approach 2:
The patent replaces manual mechanical filter design with an automated deep learning-based image restoration model. The model is trained on diverse restoration problems and automatically selects appropriate restoration strategies, eliminating the need for manual filter design while achieving superior restoration accuracy across different problem types
2Device complexity
If a single restoration model is used for all restoration types, then device complexity is reduced, but restoration precision deteriorates for specific problem types
Solution Approach 1:
The patent creates a universal image restoration model that can handle multiple restoration problem types (denoising, deblurring, inpainting, etc.) within a single framework. The model is trained on diverse datasets covering different restoration tasks and uses attention mechanisms to adaptively focus on relevant features for each specific problem type, achieving high precision across all tasks without requiring separate specialized models
3Measurement precision
If comprehensive text information is input into the restoration model, then restoration accuracy is improved through better image understanding, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by generating comprehensive text descriptions of the input image before restoration. The text encoder captures semantic information about the image content, which then guides the restoration model's attention mechanisms. This preliminary action enables the model to focus computational resources on relevant regions and features, improving accuracy while the efficient architecture minimizes the time penalty
Data Source
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AI summary
The disclose provides an image restoration method, device, apparatus and storage medium, 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. The present disclosure can greatly improve the efficiency of image restoration, more accurately restore the to-be-restored image, and obtain a restored target image with better effect.