An image de-fog method based on depth neural network

Abstract

The invention discloses an image fog removing method based on depth neural network, which comprises the following steps: selecting global atmospheric light and atmospheric scattering coefficient, generating fog map and its transmittance map by using depth of field; The training set is composed of non-fog map, fog map and transmittance map. Based on the encoder-decoder architecture, a generator network including an estimated transmittance subnetwork and a defogging subnetwork is constructed. The linear combination of antagonistic loss function, transmittance L1 norm loss function and defoggingL1 norm loss function is used to train the generator. The discriminator network is constructed based on convolution layer, sigmoid activation function and LeakyReLU function. The real non-fog map andthe de-fog map generated by the de-fog sub-network are used as positive and negative samples respectively, and the cross-entropy is used as the cost function to train the discriminator. Adopting generator and discriminator training alternately to carry on the antagonism training; After the training, a fog map to be defogged is input into the generator, and the defogging map is obtained after a forward propagation.

Description

technical field [0001] The invention relates to the fields of image processing technology and deep learning technology, in particular to an image defogging method based on a deep neural network. Background technique [0002] In the case of bad weather conditions, the images taken outdoors are often significantly degraded by the particles suspended in the air, resulting in a series of problems such as a decrease in image contrast and color distortion. Scattered by fog, haze, dust, etc. in the air, so it is the scattered light that finally reaches the camera. Haze images are usually composed of directly attenuated and scattered atmospheric light, the direct attenuation is the light intensity received by the camera after reflection and attenuation on the surface of the object, and the scattered atmospheric light is the scattered atmospheric light received by the camera. Image defogging algorithm has gradually become a research hotspot in military, aerospace, transportation and...

AI Technical Summary

Problems solved by technology

[0005] Traditional image defogging methods rely on its artificial features to calculate depth or transmittance. However, these artificial features have their own limitations, and cannot achieve satisfactory defogging effects for pictures of certain scenes.

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