Abstract: Existing image dehazing methods generally have problems such as incomplete dehazing and color distortion.Image dehazing methods based on traditional deep learning models mostly use static inference during testing,which use the same and fixed parameters for different samples,thereby inhibiting the expressive ability of the model and decreasing the dehazing performance.Aiming at the above problems,this paper proposes an adaptive image dehazing algorithm based on dynamic convolution kernel.The proposed model includes three parts:encoding network,adaptive feature enhancement network and decoding network.This paper combines dynamic convolutions,dense residual connections,and attention mechanism to complete the adaptive feature enhancement network,which mainly includes dynamic residual components and dynamic skip-connected feature fusion components.The dynamic residual component is composed of a dynamic residual dense block,a convolutional layer and a dual attention mo-dule.The dynamic residual dense block introduces dynamic convolutions into the residual dense block,and an attention-based weight dynamic aggregator is designed at the same time,which dynamically generates adaptive convolution kernel parameters.The dynamic convolutions have reduced the loss of information and enhanced the expressive ability of the model.The dual attention module combines channel attention and pixel attention to make the model pay more attention to the differences between image channels and areas with uneven distribution of haze.The dynamic skip-connected feature fusion component learns rich contextual information by dynamically fusing the features of different stages via skip-connections,preventing the early features of the network from being forgotten when the information flows into deeper layers.Meanwhile,the feature representations are greatly enriched,which benefits the restorations of the details for fog-free images.Extensive experiments on synthetic datasets and real datasets show that our method not only achieves better objective evaluation scores,but also reconstructs dehazing images with better visual effects,surpassing the performance of compared methods.

Key words: Image dehazing, Deep learning, Dynamic neural network, Attention mechanism, Feature fusion