Depth Image Filtering with Scene-Adaptive Parameter Selection
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Solution Overview
Problem
Existing depth image processing methods face challenges in achieving high-quality filtering due to the need for manual parameter setting adjustments based on scene types, leading to issues like fly pixel points, especially for users without professional experience.
Innovation Solution
Adaptive filtering is performed on depth images by determining scene types based on depth and confidence coefficients, allowing for selective filtering modes to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If general filtering algorithms (bilateral filtering, anisotropic filtering, fixed threshold filtering) are used on depth images, then filtering process can be performed, but manual parameter setting is required for different scene types which is difficult for users without professional experience and causes fly pixel points
Solution Approach 1:
The system automatically determines scene type based on depth image statistics (depth distribution, confidence coefficient distribution) and selects appropriate filtering parameters without user intervention. The algorithm self-adjusts parameters like filtering threshold and kernel size based on the detected scene characteristics, making the system easy to operate while maintaining high depth image quality
Solution Approach 2:
The system dynamically changes filtering parameters based on detected scene type. Different scene types (close-range, mid-range, far-range, complex scenes) trigger different parameter configurations including filtering threshold, kernel size, and algorithm selection. This adaptive parameter adjustment eliminates the need for manual setting while optimizing depth image quality for each specific scenario
2Manufacturing precision
If filtering parameters are manually adjusted for specific scenarios, then depth image quality can be improved, but this requires professional experience and increases operation complexity
Solution Approach 1:
The system analyzes depth image statistics (depth distribution, confidence coefficient distribution) to determine scene type and automatically selects filtering parameters. This feedback mechanism continuously monitors image characteristics and adjusts parameters accordingly, eliminating the need for professional manual adjustment while maintaining optimal depth image quality
Solution Approach 2:
The system pre-establishes multiple filtering parameter configurations corresponding to different scene types. Before actual filtering, the system determines the current scene type and selects the pre-configured parameters, avoiding the need for real-time manual adjustment and reducing operational complexity while ensuring high image quality
3Ease of operation
If adaptive filtering is implemented by determining scene type automatically, then parameter setting becomes easier, but additional processing steps are required to determine scene type
Solution Approach 1:
The system combines scene type determination and filtering parameter selection into a single integrated process. The scene type determination uses simple statistical analysis of depth distribution and confidence coefficients, which are already computed during depth image acquisition. This merging eliminates the need for separate manual parameter setting steps while keeping the overall process simple and automated
Data Source
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AI summary
The present disclosure provides a depth image processing method and apparatus, and an electronic device. The method includes: acquiring a first image acquired by a depth sensor and a second image acquired by an image sensor; determining a scene type according to the first image and the second image; and performing a filtering process on the first image according to the scene type.