Image Restoration Weight Maps for Local Degradation Emphasis
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Solution Overview
Problem
Existing image restoration methods using machine learning models do not effectively account for the non-uniform characteristics of images, leading to suboptimal performance, particularly in tasks involving local degradation, as they uniformly sample and weight all pixels without considering their intrinsic features.
Innovation Solution
A dual-machine learning model approach is employed, where a first model performs restoration processing and a second model generates a weight map to emphasize regions susceptible to degradation, optimizing the restoration process by applying different weights to pixels based on their spatial distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform sampling is used to train machine learning models for image restoration, then the training process is simple and computationally efficient, but the model performance deteriorates because it fails to account for non-uniform image characteristics and local degradation patterns
Solution Approach 1:
The patent applies local quality by introducing a weight map that assigns different weights to different spatial locations in the image based on their degradation characteristics. Instead of uniform sampling, the method identifies regions with higher degradation (such as edges, textures, or noisy areas) and samples those regions more heavily during training. This is implemented through a second machine learning model that predicts degradation maps, which then guide the sampling process for the first restoration model. This resolves the contradiction by maintaining training efficiency while significantly improving restoration performance through targeted sampling of critical regions.
2Ease of manufacture
If all pixels are sampled with equal probability in end-to-end restoration, then the implementation is straightforward, but the model cannot effectively handle local degradation patterns where some pixels are more important than others
Solution Approach 1:
The patent segments the image processing task into two distinct machine learning models: a first model that performs the actual restoration and a second model that generates weight maps indicating the importance of different pixel regions. This segmentation allows each model to specialize - the first model focuses on restoration while the second model focuses on identifying critical regions. The weight maps generated by the second model are then used to guide sampling in the first model, enabling the system to handle local degradation patterns with high precision while maintaining implementation simplicity through modular architecture.
3Reliability
If a dual-machine-learning-model approach with weight maps is implemented, then restoration performance improves by focusing on critical areas, but the model complexity increases
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of weight maps that mediate between the two machine learning models. The second model generates these weight maps as an intermediate representation of image degradation characteristics, which then guide the sampling process for the first model. This intermediary approach allows the system to capture complex degradation patterns without requiring a single monolithic complex model. The weight maps serve as a bridge that translates degradation information into sampling guidance, improving restoration performance while keeping the architecture manageable through clear functional separation.
Data Source
AI summary
A device comprising an image processor configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing, wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.