Image defogging method based on multi-scale dense connection network

Abstract

The invention discloses an image defogging method based on a multi-scale dense connection network. According to the method, images with different degrees of fog are reconstructed as relatively clear images, so that the quality and vision feeling of the images can be greatly improved. The invention discloses a self-adaptive histogram equalization mode for image preprocessing to increase contrast ratios of images, and a defogging effect can be remarkably improved; characteristics of different degrees of fog can be described by using a multi-scale dense connection convolutional nerve net, in addition, with effective combination of the characteristics, a most effective defogging effect can be achieved; an equation based on Retinex defogging problems is provided, so that end-to-end deep learning defogging can be carried out relatively concisely and effectively; compared with other defogging algorithms based on deep learning, the method has the effects that not only is the number of model parameters greatly reduced, but also an ideal defogging effect can be achieved under a condition of a very small amount of training data.

Description

Technical field [0001] The invention relates to the field of image enhancement, in particular to an image defogging method based on a multi-scale densely connected network. Background technique [0002] Fog is a weather phenomenon formed by the accumulation of tiny dust and water vapor particles under dry conditions. The turbid media such as fog, haze and smoke will absorb atmospheric light and cause the scattering of atmospheric light, which leads to degradation of the image of outdoor scenes collected in this weather. Usually, degraded images lose contrast and color fidelity. [0003] After passing through a certain scattering medium, the light intensity in the original direction will gradually weaken, and at the same time, due to the law of conservation of energy, the weakened light intensity will be scattered to other directions. In addition, the energy lost by scattering depends on the distance from the camera. Based on this physical phenomenon, people often use physical mo...

AI Technical Summary

Problems solved by technology

When the traditional method is used for image defogging, if the generation of foggy images is inconsistent with the prior conditions or assumptions of the algorithm, it will lead to a decrease in its defogging performance

When using a deep learning-based dehazing algorithm, due to its robustness limited by the data set, it will not work well when processing some images

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