Invertible Neural Network Super-Resolution for Unknown Downsampling
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Solution Overview
Problem
Traditional machine learning-based super-resolution methods rely on paired high-resolution and low-resolution images, which are often artificially synthesized, limiting their effectiveness when processing low-resolution images generated through unknown downsampling methods.
Innovation Solution
An invertible neural network is trained to generate intermediate images of a lower resolution and high-frequency information based on an input image, followed by an inverse network to produce a high-resolution output image, simulating the unknown downsampling process and conforming to a predetermined distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods use artificially synthesized image pairs for training, then the model can achieve good performance on images from the same downsampling method, but it cannot effectively process low-resolution images obtained through other downsampling methods
Solution Approach 1:
The patent introduces an intermediary variable z that conforms to a predetermined distribution, which acts as a bridge between the low-resolution image and the high-resolution output. This intermediary allows the model to handle various downsampling methods without requiring specific training data for each method, thereby resolving the contradiction between achieving good performance and maintaining adaptability across different downsampling techniques.
2Ease of manufacture
If traditional methods rely on paired high-resolution and low-resolution images for training, then the training process can be simplified, but such image pairs cannot be obtained in reality in most cases
Solution Approach 1:
The model performs self-service by generating the necessary training signals internally through the invertible neural network framework. Instead of relying on external paired images, the system creates its own training data through the transformation between low-resolution and high-resolution domains using the predetermined distribution of z, thus eliminating the need for difficult-to-obtain paired training data while maintaining training simplicity.
3Adaptability or versatility
If an invertible neural network is used to simulate unknown downsampling processes, then the method can process low-resolution images from any downsampling method, but the network architecture becomes more complex
Solution Approach 1:
The invertible neural network is designed with universal functionality that can handle various downsampling methods through a single unified framework. By incorporating the predetermined distribution of the intermediary variable z and using the inverse network to reconstruct high-resolution images, the system achieves multi-functionality in processing different downsampling techniques without requiring separate models for each method, thus balancing adaptability with manageable complexity.
Data Source
AI summary
According to implementations of the subject matter described herein, a solution is proposed for super-resolution image reconstructing. According to the solution, an input image with first resolution is obtained. An invertible neural network is trained using the input image, wherein the invertible neural network is configured to generate an intermediate image with second resolution and first high-frequency information based on the input image, the second resolution being lower than the first resolution. Subsequently, an output image with third resolution is generated based on the input image and second high-frequency information by using an inverse network of the trained invertible neural network, the second high-frequency information conforming to a predetermined distribution, and the third resolution being higher than the first resolution. The solution can effectively process a low-resolution image obtained by an unknown downsampling method, thereby obtaining a high-quality and high-resolution image.