Multi-Resolution Image Segmentation for High-Frequency Artifact Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing binary segmentation methods, particularly those based on deep learning and example-based approaches, are computationally expensive and produce low-quality results with high frequency artifacts, especially for 3D images, due to their O(n^2) complexity and reliance on large initialization datasets.
Innovation Solution
A multi-resolution iterative method that segments images by downsampling the original image and bimap to a lowest resolution, iteratively upsampling and segmenting at higher resolutions, using interactive binary segmentation algorithms to reduce computational complexity and improve quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If example-based binary segmentation methods are used, then segmentation quality improves, but computational complexity increases to O(n^2)
Solution Approach 1:
The patent applies multi-resolution segmentation by dividing the segmentation process into multiple resolution levels. The image is processed at progressively finer resolutions, with each level building upon the previous one. This divides the complex O(n^2) problem into smaller sub-problems that are computationally more manageable while maintaining segmentation quality.
Solution Approach 2:
The patent introduces a resolution dimension to the segmentation process. Instead of performing segmentation at a single resolution, it adds the dimension of progressive resolution refinement, transforming the problem from a single-step complex operation into a multi-step process that leverages information from multiple scales.
2Extent of automation
If deep learning techniques are used for segmentation, then automation improves, but computational cost increases
Solution Approach 1:
The patent segments the computational workload across multiple resolution levels, performing coarser segmentation first and refining progressively. This divides the heavy computational burden of deep learning into manageable stages, reducing peak computational cost while maintaining automation.
Solution Approach 2:
The patent performs preliminary segmentation at lower resolutions before refining at higher resolutions. This preliminary action at coarser scales provides a foundation that reduces the computational burden of subsequent finer-scale segmentation, effectively pre-processing the problem to reduce overall computational cost.
3Manufacturing precision
If high resolution segmentation is performed directly, then segmentation precision improves, but high frequency artifacts increase
Solution Approach 1:
The patent segments the frequency spectrum by processing at multiple resolution levels. Coarser resolutions capture low-frequency structural information while finer resolutions add high-frequency details progressively, preventing the accumulation of high-frequency artifacts that would occur with direct high-resolution segmentation.
Solution Approach 2:
The patent performs preliminary segmentation at lower resolutions to establish the basic structure before refining at higher resolutions. This preliminary action at coarser scales filters out high-frequency noise and artifacts, allowing subsequent refinement to focus only on genuine high-frequency details without introducing spurious artifacts.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The disclosure notably relates to a computer-implemented method for segmenting an image. The method comprises obtaining (S10) an original image (I0) to be segmented and an original bimap (Bini). The method comprises computing 520 one or more images (∀ k ∈ 1, ... , n, Ik) each by downsampling the original image (I0). The method comprises segmenting (S40) the lowest resolution image (In) based on a bimap (Bn) of the image (In) having the lowest resolution. The method comprises, iteratively (S50) for each determined image, starting from the lowest resolution image (In) and increasing the resolution (∀ k ∈ 1, ... , n in descending order) determining (S51) an upsampled segmentation mask Skuphaving a next resolution, determining (S52) a respective bimap (Bk-1) of the image (Ik-1) having the next resolution and segmenting (S53) the next resolution image (Ik-1) based on the respective bimap (Bk-1). Such a method forms an improved solution for segmenting an image.