An Image Dehazing Method Based on Multi-Scale Densely Connected Networks

Abstract

The invention discloses an image defogging method based on a multi-scale dense connection network, which reconstructs images with different degrees of fog into relatively clear images, and greatly improves the image quality and visual experience. For the first time, the adaptive histogram equalization method is proposed to improve the image contrast of the image preprocessing, which significantly improves the dehazing effect; the multi-scale densely connected convolutional neural network can describe the characteristics of fog at different scales, and effectively combine its characteristics. The most effective defogging effect is achieved; a formula based on the Retinex defogging problem is proposed, which makes the end-to-end deep learning defogging more concise and effective; the present invention is compared with other deep learning-based defogging algorithms. The invention not only greatly reduces the number of model parameters, but also can achieve an ideal defogging effect under the condition of very little 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 in dry conditions. Turbid media such as fog, haze, and smoke will absorb and scatter atmospheric light, which will degrade images of outdoor scenes collected under this weather. Typically, degraded images lose contrast and color fidelity. [0003] The light intensity in the original direction will be gradually weakened by the light passing through some kind of scattering medium, and at the same time, due to the law of energy conservation, the weakened light intensity will be scattered to other directions. Also, the energy lost by scattering depends on its distance from the camera. Based on this physical phenomenon, people often use a physical model based on atmospheric scattering ...

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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