Histogram Equalization for Color Image Visibility
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Solution Overview
Problem
Current image processing technologies fail to effectively enhance human interpretation of color images in conditions with fog, haze, smoke, dust, or low-light conditions, leading to reduced visibility and safety issues in various applications such as aviation, transportation, medical investigations, and surveillance.
Innovation Solution
The method involves histogram equalization processing of color images, including pre-washing steps and interactive control of threshold values to improve the visibility of object representations by adjusting pixel intensity distributions, allowing for better human detection in challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histogram equalization processing is applied to color images in adverse conditions, then visibility and object detection capability are improved, but image processing complexity and computational requirements increase
Solution Approach 1:
The image processing is divided into distinct stages: pre-washing step to remove unwanted color information, histogram equalization processing to enhance contrast, and selective application to different color pixel matrices. This segmentation allows each step to be optimized independently, improving overall detection capability while managing computational complexity through modular processing.
Solution Approach 2:
The patent applies histogram equalization selectively to specific color pixel matrices (e.g., green matrix alone or combined with red/blue) rather than processing all color channels uniformly. This partial action approach concentrates computational resources on the most informative channels, achieving effective object detection while reducing unnecessary processing overhead.
2Reliability
If histogram equalization is applied to enhance visibility in fog, haze, or smoke conditions, then image quality for human interpretation is improved, but processing time and computational resources increase
Solution Approach 1:
The pre-washing step is performed before histogram equalization to remove unwanted color information from fog, haze, smoke, or dust. By preparing the image data in advance through this preliminary filtering step, the subsequent histogram equalization operates on cleaner data, improving interpretation reliability while avoiding redundant processing of already-degraded color information.
Solution Approach 2:
The patent applies different processing strategies to different color pixel matrices based on their specific characteristics and information content. For example, the green matrix may be processed differently than red or blue matrices, with selective application of pre-washing and histogram equalization to channels that provide the most useful information for object detection in adverse conditions.
Data Source
AI summary
The present invention relates to image processing in general and more specifically to methods and means facilitating the human detection of physical object representations in colour images with a wide range of applications such as aviation and air transport, land transportation, shipping, submarine work, underwater inspections, medical investigations, marine archaeology, land archaeology, agriculture, surveillance and security, food safety, energy systems and forestry. The invention achieves this by providing an image processing method for a colour image representation, Ic, formed by at least two distinct colour pixel matrixes, Mi, by carrying out a histogram equalization processing step (250), which is carried out separately for each colour pixel matrix. Different pre-washing steps may be applied prior to the histogram equalization processing step (250). The invention also provides a number of apparatuses adapted for different applications using the method according to the invention.