Super-Resolution Neural Networks for Non-Integer Image Upsampling
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Solution Overview
Problem
Conventional image super-resolution systems are limited by integer up-sampling factors, requiring additional down-sampling steps and consuming excessive computational resources, while lacking efficiency in memory usage and flexibility in resolution scaling.
Innovation Solution
A super-resolution neural network integrated with a hyper neural network that parametrizes convolutional filters using a continuous family of filters, allowing non-integer up-sampling and reducing memory footprint by a factor of 40-fold or more, while achieving performance comparable to conventional systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image super-resolution systems use integer up-sampling factors, then the system structure is simple, but the resolution scaling flexibility is limited and additional down-sampling steps are required
Solution Approach 1:
The patent changes the discrete integer up-sampling factor parameter to a continuous parameter, allowing the super-resolution system to accept any positive real number as the up-sampling factor. This is achieved by modifying the network architecture to interpret the up-sampling factor as a continuous value that directly controls the feature map resolution transformation, eliminating the need for integer constraints and subsequent down-sampling operations.
Solution Approach 2:
The patent introduces dynamic adaptability by enabling the system to adjust to any up-sampling factor value dynamically without requiring structural modifications or additional processing steps. The network architecture is designed to be dynamically configurable, where the continuous up-sampling factor can be changed at runtime to achieve different resolution scaling ratios, making the system versatile for various resolution requirements.
2Manufacturing precision
If conventional systems use multiple convolutional filters for different spatial positions, then the image processing quality is improved, but the memory footprint increases significantly
Solution Approach 1:
The patent applies universality by designing a single shared convolutional filter that serves all spatial positions in the feature map, rather than having separate filters for each position. This universal filter is applied dynamically at different spatial locations through the continuous up-sampling process, reducing the number of parameters from O(H×W×K²) to O(K²) while maintaining processing quality through the continuous resolution transformation capability.
3Productivity
If conventional super-resolution systems use integer up-sampling factors, then the computational resource consumption is high due to additional down-sampling steps, but the system implementation is straightforward
Solution Approach 1:
The patent extracts and removes the unnecessary down-sampling step from the conventional super-resolution pipeline. By enabling direct continuous up-sampling, the system eliminates the redundant down-sampling operation that was required in integer-based approaches, thereby reducing computational resource consumption and simplifying the processing workflow while achieving the desired resolution scaling.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input image using a super-resolution neural network to generate an up-sampled image that is a higher resolution version of the input image. In one aspect, a method comprises: processing the input image using an encoder subnetwork of the super-resolution neural network to generate a feature map; generating an updated feature map, comprising, for each spatial position in the updated feature map: applying a convolutional filter to the feature map to generate a plurality of features corresponding to the spatial position in the updated feature map, wherein the convolutional filter is parametrized by a set of convolutional filter parameters that are generated by processing data representing the spatial position using a hyper neural network; and processing the updated feature map using a projection subnetwork of the super-resolution neural network to generate the up-sampled image.