Guided Backpropagation for Depth Map Refinement
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Solution Overview
Problem
Mobile electronic devices face challenges in efficiently processing high-resolution images due to computational expenses and the difficulty in distinguishing depths of objects with similar textures, leading to artifacts and loss of detail in depth maps.
Innovation Solution
The use of guided backpropagation-gradient updating methods that identify redundant information in images using machine learning algorithms to simplify image processing tasks and generate refined depth maps, reducing computational time and artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing operations are performed on high-resolution images, then image processing can be completed, but computational expenses are high and processing time increases
Solution Approach 1:
The patent extracts and removes redundant information from the input image before performing depth map processing. By identifying and eliminating redundant pixels or regions that do not contribute to depth information, the system reduces the computational burden while maintaining depth map accuracy, thus resolving the contradiction between processing precision and computational time
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their redundancy characteristics. Regions identified as redundant are processed differently (or skipped) compared to non-redundant regions, optimizing computational resources while maintaining overall depth map quality
2Measurement precision
If conventional image processing operations are performed on high-resolution images, then image processing can be completed, but computational expenses are high
Solution Approach 1:
The patent extracts and removes redundant information from the input image before performing depth map processing. By identifying and eliminating redundant pixels or regions that do not contribute to depth information, the system reduces the computational burden while maintaining depth map accuracy, thus resolving the contradiction between processing precision and computational time
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their redundancy characteristics. Regions identified as redundant are processed differently (or skipped) compared to non-redundant regions, optimizing computational resources while maintaining overall depth map quality
3Measurement precision
If conventional depth map processing is performed, then depth information can be obtained, but artifacts and loss of detail occur in regions with similar textures
Solution Approach 1:
The patent extracts and removes redundant information from the input image before performing depth map processing. By identifying and eliminating redundant pixels or regions that do not contribute to depth information, the system reduces the computational burden while maintaining depth map accuracy, thus resolving the contradiction between processing precision and computational time
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their redundancy characteristics. Regions identified as redundant are processed differently (or skipped) compared to non-redundant regions, optimizing computational resources while maintaining overall depth map quality
Data Source
AI summary
A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.