Transposed Convolution Upsampling for Pixel-Aligned Feature Maps
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Solution Overview
Problem
Current image processing methods, particularly in CNNs, face inefficiencies when enlarging feature maps due to the need for external interpolation, which slows processing speed and increases overhead, especially when performing transposed convolution operations.
Innovation Solution
The method involves obtaining a kernel deterministically defined based on an extension ratio to perform a transposed convolution operation, allowing for up-sampling of feature maps without external interpolation, using a neural network layer configured with kernel parameters such as size, stride, and padding, which are determined by the extension ratio, and optionally using an external information provider for interpolation if pixel alignment is not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If external interpolation is used to enlarge feature maps, then the feature map size is increased, but processing speed decreases and overhead increases
Solution Approach 1:
The patent merges the interpolation operation with the transposed convolution operation into a single unified operation. By integrating the up-sampling function directly into the convolution layer, the system eliminates the need for separate external interpolation steps, thereby maintaining processing speed while achieving feature map enlargement.
Solution Approach 2:
The transposed convolution layer is designed to perform its own interpolation function internally without requiring external assistance. The layer uses learnable parameters to automatically perform the up-sampling operation as part of its core functionality, making the system self-sufficient and eliminating dependency on external interpolation modules.
2Volume of moving object
If external interpolation is used to enlarge feature maps, then the feature map size is increased, but processing overhead increases
Solution Approach 1:
The patent combines multiple operations (convolution and interpolation) into a single integrated layer. This merging reduces the number of separate processing steps and eliminates the overhead associated with coordinating multiple independent operations, simplifying the overall system architecture.
Solution Approach 2:
The transposed convolution layer is designed to serve multiple functions simultaneously: it performs feature extraction through convolution while also performing up-sampling through its inherent interpolation capability. This multi-functionality eliminates the need for separate dedicated interpolation modules, reducing system complexity.
3Productivity
If transposed convolution operation is performed without external interpolation, then processing speed is improved, but pixel alignment accuracy may be compromised
Solution Approach 1:
The patent adjusts the parameters of the transposed convolution operation (such as kernel size, stride, and padding) to optimize both processing speed and pixel alignment accuracy. By carefully selecting these parameters, the system achieves accurate pixel alignment while maintaining high processing speed without requiring external interpolation.
Data Source
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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.