Image De-hazing via Partial Pixel Processing
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Solution Overview
Problem
Conventional image de-hazing methods require high data computation, resulting in long processing times and inefficient haze removal in outdoor scenes affected by turbid media like haze, fog, and smoke.
Innovation Solution
An image processing method that computes image intensity distribution, performs atmospheric light estimation, and estimates transmission parameters for partial pixels, allowing for efficient haze removal by applying these steps to only specific regions of the image, thereby reducing overall data computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image de-hazing methods are used, then haze removal effectiveness is improved, but data computation amount increases and processing time lengthens
Solution Approach 1:
The patent divides the image into multiple regions based on haze distribution characteristics, and applies different processing strategies to each region. This segmentation allows the system to focus computational resources on haze-affected areas while skipping clear regions, thereby maintaining de-hazing effectiveness while reducing overall computation time and data processing requirements.
2Reliability
If conventional image de-hazing methods are used, then haze removal effectiveness is improved, but data computation amount increases
Solution Approach 1:
The patent extracts and processes only the essential features and parameters needed for de-hazing from the input image, rather than performing comprehensive processing on all image data. By identifying and isolating key haze-related characteristics in specific regions, the system reduces the volume of data requiring computation while preserving the effectiveness of haze removal.
3Manufacturing precision
If processing is applied to all pixels, then processing completeness is improved, but processing time increases
Solution Approach 1:
The patent applies different processing qualities and intensities to different regions of the image based on local haze characteristics. Regions with significant haze receive full processing attention, while clear regions receive minimal or no processing. This local quality approach ensures that processing completeness is maintained where needed while reducing overall processing time across the entire image.
Data Source
AI summary
An image processing method applied to an image processing system. The image processing method comprises: (a) computing an image intensity distribution of an input image; (b) performing atmospheric light estimation to the input image; (c) performing transmission estimation according to a result of the step (a) to the input image, to generate a transmission estimation parameter; and (d) recovering scene radiance of the input image according to a result generated by the step (b) and the transmission estimation parameter. At least one of the steps (a)-(c) are performed to data corresponding to only partial pixels of the input image.