Image defogging method and corresponding image defogging device

Abstract

The invention relates to an image defogging method. The method comprises the following steps: (1)reading the image, (2)calculating and obtaining estimated value of an atmospheric light value of the image as a first estimated value of the atmospheric light value through a standard dark channel hypothetical algorithm, (3)circularly calculating and obtaining the optimum estimated value of the atmospheric light value of the image, calculating and obtaining the estimated value of the atmospheric light value of the image as a second estimated value of the atmospheric light value through a standard dark channel hypothetical algorithm in each cycle, outputting the second estimated value of the atmospheric light value obtained in the current cycle as the optimum estimated value of the atmospheric light value if difference of the first estimated value of the atmospheric light value and the second estimated value of the atmospheric light value obtained in the first cycle is not greater than the threshold value given in advance, or the difference of the second estimated value of the atmospheric light value obtained in the former cycle and the second estimated value of the atmospheric light value obtained in the current cycle is not greater than the threshold value given in advance, and implementing the next cycle if the difference is greater than the threshold value given in advance, (4)calculating the defogging image based on the optimum estimated value of atmospheric light value. The invention also relates to a corresponding image defogging device.

Description

technical field [0001] The invention relates to an image defogging method, which removes fog, haze or smoke in a single input image based on the atmospheric model theory and a dark channel assumption, so as to improve the recognizability of the scene in the image. The invention also relates to a corresponding image defogging device. Background technique [0002] Visibility enhancement of images degraded by atmospheric factors is an important field of image processing. [0003] The above atmospheric factors include fog, haze (which can be understood as lighter fog) and smoke (such as sandstorms, incomplete combustion of vehicle exhaust and smoke from chemical combustion reactions). They have common characteristics: composed of liquid or solid suspended particles, uniformly distributed in the air at least within a certain range, and form complex effects on light propagation, such as blocking, scattering and reflection. The above atmospheric factors are collectively referred ...

AI Technical Summary

Problems solved by technology

[0024] However, the dark channel assumption has limitations on how the fog acts on the fog-free image, that is, the dehazing method applying the dark channel assumption considers that the value of the fog superimposed on the three color channels is equal, that is to say, the fog is gray (white and black are two extreme values ​​of gray)

This leads to the dehazing effect of the dehazing method based on the dark channel assumption for non-gray fog, such as the yellow-brown sandstorm, etc., due to the parameter error in the estimated degeneration model.

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