Single-Model Image Restoration Across Arbitrary Resolutions
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Solution Overview
Problem
Conventional neural network models struggle to provide optimal outputs for images with resolutions different from the training resolution, and dynamically changing configurations are resource-intensive, making it difficult to handle images with arbitrary resolutions without redesigning or retraining.
Innovation Solution
A method and electronic device utilize a single trained Machine Learning (ML) model to restructure image channels based on the input layer size, allowing for image restoration, enhancement, and segmentation with arbitrary resolutions without changing the model configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional neural network model is trained with images of a constant resolution, then the model provides optimal output for images with that specific resolution, but the model fails to provide optimal output for images with higher or lower resolutions
Solution Approach 1:
The patent applies segmentation by dividing the input image into multiple patches or regions that are then processed individually through the neural network. This allows the model to handle images of arbitrary resolution by breaking them down into manageable units, thereby maintaining output quality across different resolutions without requiring retraining.
Solution Approach 2:
The patent introduces a new dimension by adding a resolution encoding layer or auxiliary input that provides resolution information to the network. This allows the model to adapt to different resolutions by incorporating resolution as an additional input dimension, enabling the same model to optimize outputs across varying image sizes.
2Adaptability or versatility
If a multiscale neural network model is used to handle higher resolution images, then the model can process varying resolutions, but the system requires dynamically changing configurations which is resource-intensive and not feasible for on-device implementations
Solution Approach 1:
The patent implements a universal model architecture that can process images of any resolution using the same fixed configuration. By designing the network to accept variable-sized inputs through mechanisms like patch-based processing or resolution encoding, the single model performs multiple resolution-handling functions without requiring dynamic reconfiguration or multiple specialized models.
Solution Approach 2:
The patent changes the approach from dynamically changing model architecture parameters to fixing the model parameters and instead adapting to different resolutions through input preprocessing or auxiliary resolution inputs. This allows the model to handle varying resolutions by changing how the input is prepared or represented rather than changing the model structure itself.
3Measurement precision
If multiple Deep Neural Networks are used to perform similar tasks for different resolutions, then each task can be optimized, but the model memory footprint on the device increases
Solution Approach 1:
The patent merges multiple resolution-specific models into a single unified model that can handle all resolutions. By combining the functionality of what would otherwise require separate models into one architecture that processes different resolutions through a common framework, the total memory footprint is reduced while maintaining the ability to perform task-optimized processing for each resolution type.
Data Source
AI summary
A method for performing multi-functional image restoration by an electronic device with a trained Machine Learning (ML) model is provided. The method includes receiving an image and determining channels of the image, and determining whether a number of restructuring needed for the channels is one. When the restructuring needed for the channels not one, then the method includes restructuring each channel into a first channel set, generating first inferences of the image corresponding to each channel by feeding the first channel set to the trained ML model, and generating a final inference image by combining the first inferences. When the number of restructuring needed for the channels is one, then the method includes restructuring the channels into a second channel set, and generating a second inference of the image by feeding the second channel set to the trained ML model.