Infrared Image Processing for Fog Visibility
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Solution Overview
Problem
Infrared images captured in foggy or precipitation conditions suffer from degraded visibility, making it difficult to reliably identify objects, particularly in applications like autonomous vehicle navigation.
Innovation Solution
An image processing method that compensates for fog-induced intensity loss by calculating corrective factors for each pixel row, based on the average deviation of pixel intensities, to enhance image quality and object visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images are acquired in foggy or precipitation conditions, then the camera can capture scenes in adverse weather, but the visibility of objects in the images is degraded
Solution Approach 1:
The patent applies parameter changes by computing row-specific multiplicative coefficients that adjust pixel intensities based on fog-induced attenuation models. The coefficient for each row is calculated as exp(k*(y_j - y_ref)), where k is the attenuation coefficient and y coordinates represent vertical position. This transforms the image parameters to compensate for fog effects and restore object visibility.
Solution Approach 2:
The patent introduces an intermediary processing step that models the physical interaction between fog and infrared radiation. By calculating attenuation coefficients and applying row-specific correction factors, the method acts as an intermediary between the degraded captured image and the restored visibility, effectively mediating the information loss caused by fog.
2Device complexity
If standard image processing methods are used, then the processing is simple, but the visibility improvement in foggy conditions is insufficient
Solution Approach 1:
The patent segments the image processing into row-specific operations, where each row is processed independently with its own multiplicative coefficient. This segmentation allows the complex fog compensation to be applied in a systematic, row-by-row manner, maintaining computational efficiency while achieving effective visibility restoration throughout the entire image.
Data Source
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AI summary
Disclosed is an image processing method comprising the following steps: a) acquiring an initial image (A); c1) using at least one computer, for each value of an index j in a range of lines going from N1 to N2 of the initial image A, determining a factor F1(j) such that: Emean (G1(j); IA,i,j *F1(j); IA,i,j+s) ≤ Emean (G1(j); IA,i,j; IA,i,j+s) in which G1(j) is a first group of columns of line j under consideration; IA,i,j is an intensity of a pixel of the initial image A located on column i and line j; Emean (G1(j); x(i,j); y(i,j)) is the mean deviation in absolute value between values x(i,j) and y(i,j), for the values of i of the group of columns G1(j); s=+1 or -1; and d1) calculating a first improved image (B1) in which each line Lj of the range of lines going from N1 to N2, is replaced by a line L'j obtained by multiplying each pixel of line Lj by a multiplier coefficient (M1(j)) equal to the product of factors F1(k), for k going from N1 to j. The invention also relates to a device for implementing said method.