Hybrid Depth Estimation for Accurate 3D Image Display
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Solution Overview
Problem
Current image processing systems for capturing and displaying three-dimensional images often fail to accurately correlate with real-world depth, leading to decreased effectiveness in displaying meaningful information, particularly under competitive market pressures and consumer expectations.
Innovation Solution
The system processes an image sequence by forming image pairs, calculating iteration numbers for blur differences, determining zero-crossing points, and generating depth maps to create a display image that accurately represents depth, using a combination of modules for capturing, processing, and displaying images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple lenses are used to capture stereoscopic images for three-dimensional display, then depth information is enhanced, but device complexity and cost increase
Solution Approach 1:
The patent divides the image processing into discrete grid blocks and processes depth information separately for each block. The image is segmented into multiple regions, and depth maps are calculated independently for each segment, allowing complex processing to be broken down into manageable units that reduce overall system complexity.
Solution Approach 2:
The patent introduces an intermediary computational process using blur metric analysis and iterative convolution to derive depth information from single-lens captured images. This intermediary method acts as a mediator that transforms ordinary 2D image data into 3D depth representations without requiring additional optical hardware.
2Measurement precision
If complex image processing algorithms are applied to improve depth map accuracy, then depth representation quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating blur metrics for each grid block before full depth map generation. The system prepares intermediate results including blur difference calculations and iterative convolution outputs in advance, which are then used to construct the final depth map more efficiently, reducing overall processing time.
Solution Approach 2:
The patent applies partial action by focusing computational resources on calculating depth information only for regions where blur differences indicate depth variation. Instead of processing the entire image uniformly, the system selectively applies complex algorithms only where needed, based on local blur metric analysis, thereby reducing total computation time while maintaining accuracy where it matters most.
3Measurement precision
If iterative convolution with blur kernel is performed to calculate blur difference, then depth measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the image into grid blocks and performs iterative convolution independently on each block rather than on the entire image. This segmentation reduces the computational complexity of each individual convolution operation while maintaining overall precision through the aggregation of block-level results into the complete depth map.
Data Source
AI summary
A system and method of operation of an image processing system includes: a capture image sequence module for receiving an image sequence; a calculate iteration depth map module for calculating an iteration depth map and a zero-crossing array; a calculate median depth map module for calculating a median depth map and a depth difference map; a calculate variance depth map module for calculating a variance depth map; a calculate image depth map module for calculating an image depth map based on the iteration depth map, the zero-crossing array, the median depth map, the depth difference map, and the variance depth map; and a calculate display image module for calculating a display image based on the received images and the image depth map for displaying on a display device.