Aiming at the problems such as halos, artifacts and incomplete dehazing in hazy image restoring processing, a dehazing algorithm of compensated transmission based on negative haze concentration correction is proposed. First of all, the error mechanism is used to compensate for the transmission of the dark channel prior(DCP), observing the relationships among transmission, depth of field, and haze concentration. A negative haze concentration model is constructed to adaptively correct the transmission of gamma in this study. Finally, the channel difference fusion-based median channel is proposed to correct local atmospheric veil and combined with the atmospheric scattering model to recover haze-free image. The experimental results show that the algorithm solves the problems of halos, artifacts and incomplete dehazing with outstanding details and appropriate brightness.

Convolutional neural network is developing rapidly in image processing. Most image dehazing algorithms only focus on dehazing but neglect the overall quality of dehazing image, which leads to problems such as loss of information blurred texture, etc. To solve these problems, we propose a dehazing and enhancement convolutional neural network. Hazy image and clear image are obtained by encoding and decoding. Enhancement network is used to restore the texture and details of dehazing image. Experiments show that the proposed method has excellent results in subjective evaluation and quality indexes. Haze can be removed more thoroughly, and images with clearer details and texture can be obtained.

Aiming at the problems such as color cast and incomplete haze removal in dehazing algorithms, a multi-level feature fusion network based on the learning of hazy layers is proposed for single image dehazing. Firstly, a difference image between hazy image and haze-free image is defined as the hazy layer via atmospheric scattering model, and the effective estimation of the hazy layer could be used to optimize dehazing effect. Then, an end-to-end network model is designed, which mainly includes a hazy layer estimation module and an image restoration module. In hazy layer estimation module, the low-level and high-level features of image are extracted through feature extraction blocks, and a multi-level fusion strategy is used to add the features of different levels pixel by pixel to achieve feature fusion. The fused hazy layer contains both local and global information. Finally, the hazy layer is directly subtracted from hazy image to achieve the effective restoration of haze-free image according to image restoration module. Experiments show that the proposed algorithm can obtain clear and natural results, and the color cast phenomenon is effectively avoided compared with existing dehazing methods. The objective evaluation indicators on synthetic images and real images further verify the effectiveness of the proposed algorithm.

Aiming at the problems of imprecise transmission estimation and color cast in single image dehazing algorithms, an image dehazing method combining adaptive dual transmissions and scene depth variation is proposed. Firstly, a haze image is converted from RGB color space to Lab color space, morphological processing and filtering operation are performed on the luminance component, and the atmospheric light is estimated in combination with the maximum channel. Secondly, a Gaussian-logarithmic mapping of haze image is used to estimate the dark channel of haze-free image, and the bright channel of haze-free image is obtained by using the inequality relation of atmospheric scattering model. Thus, the dual transmissions are gotten. Finally, an adaptive transmission map with joint optimization of dual transmissions is constructed according to the relationship between depth map and transmission. A high-quality haze-free image can be directly recovered by using the proposed method with the atmospheric scattering model. The experiments show that the recovery results have natural color, thorough dehazing effect, rich detail information and high visual contrast. Meanwhile, good dehazing effects can be gotten in different scenes.

In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging, a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed. Firstly, the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel, and used the morphological edge to set the scale of the open operation structure elements, and constructed a multi-scale open operation fusion dark channel. After morphological noise reduction, the exact initial transmittance was obtained. According to the relationship between brightness and saturation difference and transmittance, an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map. Then the local atmospheric light was improved according to the image brightness information and morphology closure operation. Finally, the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image. The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes, the restoration effect was good, and the brightness was suitable.

Current deep learning-based image dehazing algorithms usually use traditional convolutional layers when extracting features, which easily cause loss of information, such as details and edges of the image, ignore the location information of the image in feature extraction, and neglect the original information of the image in feature fusion, and they thus fail to recover a high-quality dehazing image with complete and clear structure. To address this problem, a dehazing algorithm based on residual contextual attention and cross-layer feature fusion was proposed. Firstly, the residual group structure was obtained by serializing the proposed residual contextual blocks, and feature extraction was performed on the first two layers of the network, i.e. the shallow layers, to obtain rich contextual information in the shallow layers; secondly, coordinate attention was introduced to build an attention graph with location information and apply it to the residual contextual feature extraction, which was placed in the third layer of the network, i.e. the deep layer, to extract deeper semantic information; then, by fusing feature information from different resolution streams across layers in the middle layer of the network, the information exchange between the deep and shallow layers was enhanced to achieve feature enhancement; finally, the semantic information-rich features obtained from the network were combined with the original input, thus enhancing the recovery effect. Experimental results on the RESIDE dataset and the Haze4K dataset show that the proposed algorithm achieves better results in terms of visual effects and objective metrics.