Image Processing Apparatus Combining AI and Non-AI Images for Distance Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing systems struggle to effectively combine and enhance images using AI and non-AI processing methods, particularly in accurately representing distance and reliability across different image regions, leading to suboptimal composite images.
Innovation Solution
An image processing apparatus that acquires AI-processed and non-AI-processed images, aligns their dimensions, and combines them based on reliability degrees derived from a reliability map, using a neural network to determine accurate representation and weighting for edge and non-edge regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI processing is applied to generate a distance image, then image quality and distance representation are improved, but processing time and computational complexity increase
Solution Approach 1:
The image is divided into edge regions and non-edge regions, with different processing strategies applied to each. Edge regions use non-AI processing for speed, while non-edge regions use AI processing for accuracy, resolving the contradiction between processing time and distance representation accuracy.
Solution Approach 2:
Different processing methods are applied to different regions of the image based on their characteristics. The neural network specifically targets non-edge regions where distance information is more critical, while edge regions are processed more quickly, optimizing both speed and accuracy locally.
2Measurement precision
If AI processing is applied to all image regions, then distance image accuracy is improved, but processing complexity and resource consumption increase
Solution Approach 1:
The processing system is segmented into multiple pathways: edge detection using traditional methods, neural network processing for non-edge regions, and a composition unit that merges results. This segmentation reduces overall processing complexity while maintaining accuracy where needed.
Solution Approach 2:
Instead of applying AI processing to the entire image, the system applies it partially only to non-edge regions where it provides the most value. This partial action reduces computational complexity and resource consumption while still achieving high distance image accuracy in critical areas.
3Measurement precision
If composite image is created by combining AI-processed and non-AI-processed images, then image quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by detecting edge regions and generating a mask before the main composition process. This preliminary action allows the subsequent combining of AI and non-AI processed images to proceed more efficiently, reducing overall processing time while maintaining high composite image quality.
Data Source
AI summary
The image processing apparatus includes a processor. The processor is configured to: acquire a second image, which is obtained by performing first AI processing on a first image, and a fourth image, which is obtained without performing the first AI processing on the first image or a third image; and combine the second image and the fourth image according to an indicator of the first image and/or the third image.