Haze Transmittance Detection Using Depth Information
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Solution Overview
Problem
Existing methods for estimating transmittance in images with haze, such as those using the dark channel prior, struggle to accurately measure transmittance in white or light gray subject regions due to assumptions about color channel intensities.
Innovation Solution
An information processing apparatus and method that detects transmittance by using estimated transmittance values from captured images and depth information for each region, calculating an atmospheric scattering coefficient and employing grayscale conversion to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the dark channel prior method is used to estimate transmittance, then the measurement process is simple, but the transmittance detection accuracy deteriorates in white or light gray subject regions
Solution Approach 1:
The image is divided into multiple local regions, and transmittance estimation is performed separately for each region using dark channel prior. This segmentation allows the method to handle different regions with different characteristics, improving overall accuracy while maintaining simplicity.
Solution Approach 2:
Depth information is introduced as an intermediary element to guide the transmittance estimation process. The depth map provides spatial context that helps distinguish between actual scene structure and haze effects, enabling more accurate transmittance detection in challenging regions.
2Productivity
If the dark channel prior assumes at least one color channel has lower intensity, then the method is computationally efficient, but it becomes impossible to accurately measure transmittance in white or light gray subject regions
Solution Approach 1:
The assumption of dark channel prior is applied locally to each region rather than globally to the entire image. This allows the method to maintain computational efficiency while adapting to local variations in scene content, particularly handling white or light gray regions more accurately by considering their specific contextual information.
Solution Approach 2:
The transmittance estimation process is made dynamic by using depth information to adjust the estimation for each region. Rather than applying a static assumption uniformly, the method dynamically adapts the estimation based on depth cues, maintaining efficiency while improving accuracy in varying conditions.
Data Source
AI summary
A transmittance estimation unit estimates a transmittance for each of regions from a captured image. The transmittance estimation unit estimates the transmittance for each pixel, using, for example, dark channel processing. A transmittance detection unit detects a transmittance of haze at the time of imaging of the captured image, using the transmittance estimated for each pixel by the transmittance estimation unit and depth information for each pixel. The transmittance detection unit detects the transmittance of the haze on the basis of, for example, a logarithm average value of the transmittances in the whole or a predetermined portion of the captured image, and an average value of depths indicated by the depth information. Alternatively, the transmittance detection unit converts a grayscale of the transmittance estimated for each of the regions from the captured image into a grayscale of the depth indicated by the depth information for each of the regions and sets the transmittance after the grayscale conversion as the transmission of the haze. This enables the transmittance to be detected with high accuracy.