The invention discloses an image defogging method for
haze concentration of an adaptive neural network based on end-to-end. The method comprises the following steps: constructing an image defogging model; acquiring foggy image data; using a feature enhancement module in the image defogging model for cascading the feature map with images recovered by different paths, combining fuzzy images with different dense
haze degrees to help the network sense image
haze concentration in a self-adaptive mode; reconstructing the features after function enhancement into a clear
fog-free image through a multi-scale feature attention module; calculating
mean square errors and
perception losses of the restored images and the corresponding clear images, and updating an image defogging model; wherein the meansquare errors guide the image defogging model to learn the content of the clear images, the
perception loss is used for quantizing the visual difference between the recovered images and the corresponding clear images, and the two loss functions cooperatively optimize the defogging model. According to the technical scheme, effective defogging
processing is carried out on the actually shot
fog image, a high-quality image is recovered, and the practicability is good.