Super Resolution Neural Network with Shared Feature Extraction Layers
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Solution Overview
Problem
Current super-resolution image processing techniques, including SRGANs, are limited in their ability to upscale images by factors greater than 4X, as they are trained for fixed upscaling factors and cannot achieve the same image quality when using additional methods like the nearest neighbor approach for larger factors.
Innovation Solution
A method and system that utilize a super-resolution neural network with multiple sets of upscaling layers sharing feature extraction layers, allowing for upscaling by different factors, and an additional module for further upscaling if necessary, to achieve a desired final upscaling factor, enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a super-resolution neural network is trained for a fixed upscaling factor (e.g., 4X), then image quality is optimized for that specific factor, but the system cannot achieve the same image quality when upscaling by larger factors (greater than 4X)
Solution Approach 1:
The upscaling process is segmented into multiple stages by dividing the upscaling layers into different sets, where each set is trained for a specific upscaling factor. This allows the system to handle different upscaling factors (2X, 4X, 8X, 16X) by selecting the appropriate pre-trained set, thereby maintaining image quality across various upscaling factors without requiring retraining.
Solution Approach 2:
The neural network is designed with multiple sets of upscaling layers that share the same feature extraction layers, enabling a single model architecture to perform multiple upscaling functions. Each upscaling layer set is trained for a specific factor but can be selectively applied to achieve different upscaling factors, making the system universally applicable to various upscaling needs while maintaining optimized image quality for each factor.
2Adaptability or versatility
If multiple sets of upscaling layers are added to handle different upscaling factors, then upscaling flexibility is improved, but the device complexity increases
Solution Approach 1:
Multiple sets of upscaling layers are merged into a single neural network architecture by sharing the feature extraction layers among all sets. This merging approach allows the system to handle multiple upscaling factors (2X, 4X, 8X, 16X) within a unified model structure, reducing overall complexity compared to having separate models for each upscaling factor while maintaining the flexibility to select the appropriate upscaling set.
3Adaptability or versatility
If the nearest neighbor approach is used for upscaling by factors greater than 4X, then the system can handle larger upscaling factors, but image quality deteriorates
Solution Approach 1:
Multiple sets of upscaling layers are pre-trained for specific upscaling factors (2X, 4X, 8X, 16X) before being used in the actual image processing task. When an image needs to be upscaled by a factor greater than 4X, the system selects the pre-trained set with the closest matching factor, ensuring that high-quality super-resolution performance is maintained without falling back to the nearest neighbor approach, thereby preventing image quality deterioration.
Data Source
AI summary
Systems and methods upscale an input image by a final upscaling factor. The systems and methods employ a first module implementing a super resolution neural network with feature extraction layers and multiple sets of upscaling layers sharing the feature extraction layers. The multiple sets of upscaling layers upscale the input image according to different respective upscaling factors to produce respective first module outputs. The systems and methods select the first module output with the respective upscaling factor closest to the final upscaling factor. If the respective upscaling factor for the selected first module output is equal to the final upscaling factor, the systems and methods output the selected first module output. Otherwise, the systems and methods provide the selected first module output to a second module that upscales the selected first module output to produce a second module output corresponding to the input image upscaled by the final upscaling factor.