Neural Network Defocus Estimation for Obstruction Handling
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately focusing on a subject, especially when obstructions like arms or hands cover the face, and in low-light or high-contrast environments, leading to errors in focus detection due to variations in distance values.
Innovation Solution
An image processing apparatus utilizing a neural network to estimate defocus amounts from defocus maps, integrating features generated from images, defocus maps, and subject region maps to improve focus adjustment accuracy, while reducing the influence of obstructions and focus detection errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical values of distance values are used to determine obstruction regions, then focus adjustment can be performed, but measurement precision deteriorates due to variations in distance values in low-light or high-contrast environments
Solution Approach 1:
The patent introduces a neural network as an intermediary component that processes defocus map data to estimate subject defocus amounts. Instead of directly using statistical values of distance values (which are prone to errors in low-light/high-contrast conditions), the neural network acts as a mediator that transforms the defocus map into reliable defocus amount estimates, filtering out the influence of obstructions and environmental variations.
Solution Approach 2:
The patent replaces the traditional mechanical/statistical approach (using statistical values of distance values) with a neural network-based system. This substitution allows the system to process defocus map information through learned patterns rather than relying on direct statistical calculations, thereby improving measurement precision in challenging lighting conditions.
2Measurement precision
If defocus amounts are estimated using traditional methods, then processing speed is maintained, but measurement precision deteriorates in low-light or high-contrast environments
Solution Approach 1:
The patent performs preliminary action by pre-processing the defocus map data through neural network training before actual focus adjustment is needed. The neural network learns patterns from training data and can quickly estimate defocus amounts during inference without requiring complex real-time calculations, thus achieving high precision while maintaining processing speed.
3Adaptability or versatility
If obstructions are present in the image, then focus detection can still be performed, but measurement precision deteriorates due to errors in distance values
Solution Approach 1:
The patent extracts and eliminates the influence of obstructions from the focus detection process. By using neural networks to process defocus map data, the system can identify and exclude regions affected by obstructions (such as arms or hands blocking the face) from the distance value calculations, thereby maintaining measurement precision even when obstructions are present in the image.
Data Source
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AI summary
An image processing apparatus (2, 13) is provided. The apparatus acquires (S301, S302, S303, S901) input data including a captured image and/or information relating to the captured image. The apparatus acquires (S305, S903) a feature of the input data by performing processing on the input data using a neural network. The apparatus generates (S306, S904) an integrated feature by integrating the feature and at least some of the input data. The apparatus generates (S307, S905) an estimation result of at least one of a defocus range and a depth range for a subject within the captured image, by performing processing on the integrated feature.