Deep Guided Filtering With Low-Dimensional Guidance for Segmentation
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Solution Overview
Problem
Neural network-based semantic segmentation processes face challenges in achieving accuracy in semantic boundary regions while incurring high computational complexity and resource consumption.
Innovation Solution
Implementing a deep guided filter (DGF) with a lower resolution guidance, which includes a lower resolution guidance, and optimizing the guidance to reduce the computational complexity, and optimizing the guidance to reduce the computational complexity, and optimizing the guidance to improve the computational efficiency, and optimizing the guidance to improve the computational efficiency, and optimizing the guidance to improve the computational efficacy, and optimizing the guidance to enhance the computational effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep guided filter methods are used for semantic segmentation, then accuracy in semantic boundary regions can be achieved, but computational complexity increases significantly requiring more time and computing resources
Solution Approach 1:
The patent divides the feature processing into multiple dimensionalities, separating guidance features (lower dimensionality) from input features (higher dimensionality). This segmentation allows the computationally intensive filtering operations to be performed on lower dimensionality data while maintaining accuracy on the full-resolution features, thus reducing overall computational complexity while preserving boundary accuracy.
Solution Approach 2:
The patent transforms the problem by operating in a lower dimensionality space for guidance generation. Instead of applying deep guided filters directly to full-resolution features, it generates guidance maps at reduced dimensionality and then applies the filtering. This dimensionality change significantly reduces the computational burden while maintaining the effectiveness of semantic boundary preservation through subsequent upsampling operations.
2Measurement precision
If traditional deep guided filter methods are used for image processing, then semantic segmentation accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the processing pipeline into distinct stages: guidance generation at lower dimensionality, filter application on reduced-size data, and result upsampling to full resolution. This temporal segmentation allows computationally expensive operations to occur on smaller data sets, reducing processing time while maintaining final output accuracy through the upsampling step that restores full-resolution semantic boundaries.
Solution Approach 2:
The patent exploits dimensionality reduction to accelerate processing. By performing deep guided filtering operations on lower dimensionality guidance maps rather than full-resolution features, the processing time is significantly reduced. The accuracy is preserved by upsampling the filtered guidance back to full resolution before final segmentation, effectively trading temporary dimensionality reduction for speed without sacrificing final accuracy.
3Measurement precision
If full dimensionality features are used for deep guided filtering, then processing accuracy is maintained, but computational resources required increase
Solution Approach 1:
The patent segments the feature space into guidance features and input features with different dimensionalities. The computationally intensive deep guided filtering operations are applied only to the lower dimensionality guidance features, consuming fewer computational resources. The full dimensionality accuracy is preserved by applying the filtered guidance to the complete input features in the final segmentation step, effectively separating the computational burden from the accuracy requirement.
Solution Approach 2:
The patent transitions to a lower dimensionality representation for the guidance generation and filtering stages. By generating guidance maps at reduced dimensionality and performing filtering operations in this compressed space, computational resource consumption is dramatically reduced. The full-resolution accuracy is recovered through upsampling operations that restore the filtered guidance to original dimensions before final semantic segmentation, making the approach energy-efficient without sacrificing precision.
Data Source
AI summary
A method of image processing includes: determining a first feature, wherein the first feature has a dimensionality D1; determining a second feature, wherein the second feature has a dimensionality D2 and is based on an output of a feature extraction network; generating a third feature by processing the first feature, the third feature having a dimensionality D3; generating a guidance by processing the second feature, the guidance having the dimensionality D3; generating a filter output by applying a deep guided filter (DGF) to the third feature using the guidance; generating a map based on the filter output; and outputting a processed image based on the map.