Neural Network Transposed Convolution Upsampling
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Solution Overview
Problem
Existing image processing methods, particularly in CNNs, face inefficiencies in up-sampling feature maps due to reliance on external information providers for interpolation operations, leading to processing speed decreases and overheads.
Innovation Solution
The method involves deterministically defining a kernel based on an extension ratio for transposed convolution operations, allowing for up-sampling of feature maps within a neural network layer without external interpolation, using weights inversely proportional to pixel distances and dynamically generating kernel parameters for efficient pixel alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If external information providers are used for interpolation operations during up-sampling, then the up-sampling can be performed, but processing speed decreases and overhead increases
Solution Approach 1:
The patent extracts the interpolation operation from the external information provider and integrates it into the neural network layer itself. The up-sampling operation is performed by the neural network using its own computational resources, eliminating the need for external intervention and reducing system overhead while maintaining processing speed.
Solution Approach 2:
The patent merges the up-sampling function with the neural network layer by implementing transposed convolution operations directly within the network architecture. This consolidation allows the neural network to perform both feature extraction and up-sampling in a unified computational framework, eliminating the separate interpolation step and reducing overhead.
2Productivity
If transposed convolution operations are performed within the neural network layer without external interpolation, then processing efficiency improves, but precise pixel alignment becomes more challenging
Solution Approach 1:
The patent adjusts the parameters of the transposed convolution operation, specifically the kernel size and stride, to achieve precise pixel alignment. By carefully selecting these parameters based on the desired up-sampling factor, the system maintains both high processing efficiency and accurate pixel positioning without requiring external interpolation.
3Manufacturing precision
If kernel size is increased to improve up-sampling quality, then the resolution of the second feature map improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by using a kernel size that is sufficient for achieving the desired up-sampling quality but not excessively large. The kernel dimensions are carefully chosen to match the up-sampling factor (e.g., 2x2 kernel for 2x up-sampling), providing just enough computational effort to achieve good results without unnecessary complexity.
Data Source
AI summary
An Image processing method and apparatus are provided. The Image processing method includes obtaining a kernel that is deterministically defined based on an extension ratio of a first feature map, up-sampling the first feature map to a second feature map by performing a transposed convolution operation between the first feature map and the kernel, and outputting the second feature map.