Multiview Image Hole Restoration Using Gradient-Based Structural Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods face challenges in restoring holes in multiview images while maintaining structural integrity and visual naturalness, especially when generating 3D displays from a limited number of input images.
Innovation Solution
The method involves determining candidate blocks for restoration based on a similar form distribution feature, which includes structure information from the background, to restore hole regions while maintaining the background's structure, thereby reducing operational complexity and preventing image degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image completion methods are used to restore holes in multiview images, then hole restoration is achieved, but structural integrity and visual naturalness of the background deteriorate
Solution Approach 1:
The patent applies local quality by using gradient information and edge detection to identify specific regions with different structural characteristics. The restoration process adapts to local structures by analyzing gradient directions and magnitudes in different areas, allowing the algorithm to preserve edges and structural boundaries while filling hole regions, thereby maintaining both structural integrity and visual naturalness
Solution Approach 2:
The patent segments the image processing task into distinct stages: edge detection, gradient calculation, candidate block selection, and restoration. By dividing the hole restoration process into these sequential steps, the method can systematically preserve structural information while achieving visually natural results, resolving the contradiction between structural integrity and visual quality
2Reliability
If complex image restoration algorithms are applied to maintain structural integrity, then structural integrity is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing computational efforts only on critical regions - specifically calculating gradients and detecting edges only at boundaries and candidate block locations rather than processing the entire image. This selective approach maintains structural integrity through targeted analysis while significantly reducing overall processing time and computational resource requirements
3Manufacturing precision
If extensive candidate blocks are evaluated to ensure visual naturalness, then visual naturalness is improved, but operational complexity increases
Solution Approach 1:
The patent changes the evaluation parameters for candidate block selection by using gradient magnitude and direction as primary criteria instead of exhaustive visual similarity metrics. This parameter transformation simplifies the operational complexity while maintaining visual naturalness, as gradient-based evaluation provides a computationally efficient way to identify structurally appropriate candidate blocks without requiring complex comparison algorithms
Data Source
Figure 1
Figure 2A~2B
Figure 2C
AI summary
An image processing method includes acquiring a feature defining a distribution of similar forms in a first image; and restoring a hole included in a second image based on the feature.