Haze Removal Using Fuzzy Membership Function
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Solution Overview
Problem
Conventional haze removal methods using hazy models often result in halo artifacts, degrading image quality and causing loss of color contrast, which are critical issues in image processing for applications like autonomous vehicles and surveillance systems.
Innovation Solution
An apparatus and method employing a fuzzy membership function to calculate a transmission map, which refines the image by determining pixel values based on brightness values and selecting specific areas within the image to remove haze without inducing halo artifacts or losing color contrast, utilizing a processor to execute these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional haze removal methods using hazy models are applied, then haze can be removed from images, but halo artifacts occur and color contrast is lost
Solution Approach 1:
The patent changes the fundamental parameter of transmission map calculation by introducing a fuzzy membership function that considers both brightness values and local contrast. This transforms the transmission map from a simple brightness-based calculation to a multi-parameter fuzzy logic calculation, thereby removing haze while preserving color contrast and avoiding halo artifacts through enhanced parameter consideration
2Reliability
If transmission map is calculated using conventional methods, then haze removal is achieved, but color contrast is lost
Solution Approach 1:
The patent modifies the transmission map calculation parameters by incorporating local contrast information through fuzzy membership functions. This parameter enhancement ensures that color contrast is preserved during haze removal by adjusting the transmission values based on both brightness and contrast characteristics of different image regions
3Reliability
If kernel operation is used in haze removal, then haze can be removed, but halo artifacts are generated
Solution Approach 1:
The patent changes the calculation parameters of the transmission map by integrating fuzzy logic that considers local contrast and brightness relationships. This parameter transformation eliminates the need for kernel operations that cause halo artifacts, as the fuzzy-based transmission map inherently preserves edge information and avoids the smoothing effects that create halos
Data Source
AI summary
The apparatus for removing haze from an image using a fuzzy membership function includes memory configured to store computer-readable instructions; and a processor configured to execute the instructions, wherein the processor calculates a hazy value and a transmission map using an input image including a hazy component and generates a restored image from which the hazy component has been removed using the hazy value and the transmission map, and wherein the processor selects a first area in the input image, calculates a fuzzy membership function of pixels of the input image with respect to the hazy value using brightness values of the pixels of the input image and brightness values of pixels of the first area, and calculates the transmission map using the brightness values of pixels of the first area and the fuzzy membership function.