Objective

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The acquired images relating fog

mist and damp weather conditions are subjected to the atmospheric scattering

affecting the observation and target analysis of intelligent detection systems. The scattering of reflected lights deduct the contrast of images in the context of the increased scene depth

and the uneven sky illumination constrains images visibility. This two constraints yield to deduction and fuzzes on the weak texture in foggy images. The degradation of foggy images affects the pixels based statistical distributions like saturation and weber contrast and changes the statistical distribution between pixels

such as target contour intensity. Thus

visual perception quality related to natural scene statistical (NSS) features of fog image and the target detection accuracy related target category semantic (TCS) features are significantly different with ground truth

Traditional image restoration methods can build the defogging mapping to improve image contrast based on the conventional scattering model. But

it is challenged to remove the severe scattering of image features. Deep learning based image enhancement methods have better scattering image removal results close to the distribution of training data. It is a challenged issue of insufficient generalization ability like dense artifacts for ground truth foggy images derived of complex degradation the degradation in synthetic foggy images excluded. Current methods have focused on semi-supervision technique based generalization ability improvement but the large domain distance constrains between real and synthetic foggy images existing. To optimize the image features

current deep learning based methods are challenged to achieve the niches between visual quality and machine perception quality via image classification or target detection. To interpret pros and cons of prior-based and deep leaning based methods

a semi-supervision prior hyrid network for feature enhancement is illustrated to demonstrate the feature enhancement for detection and object analysis.

Method

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Our research is designated to a semi-supervision prior hybrid network for NSS and TCS feature enhancement. First

a prior based fog inversion module is used to remove the atmospheric scattering effect and restore the uneven illumination in foggy images. The method is based on an extended atmospheric scattering model and a regional gradient constrained prior for transmission estimation and illumination decomposition. Then

a feature enhancement module is designed based on the condition generative adversarial network (CGAN) as a subsequent module

which regards the defogged image as the input image. The generator uses six Res-blocks with the instance norm layer and a long skip connection to achieve the image domain translation of defogged images. There are three discriminators in the network. The style and feature discriminator with five convolution layers and leakReLU layers are used to identify the image style between "defogged" and "clear"

promoting the generator using the adversarial technique with CGAN loss in pixel-level and feature-level. Excluded the CGAN loss for further removing the scattering degradation in defogged images

the generator is also trained based on a content loss

which constrains the details distortion in the image translation process. Moreover

our research analyzes the domain differences between the defogged image and the clear image in the target feature level

and utilizes a target cumulative vector loss based a target sematic discriminator to guide the refinement of target outline in the defogged image. Thus

the feature enhancement module is implemented to contrast and brightness related NSS features and improve the performance of TCS features about target clearance. The reversed features are constrained to match the CGAN loss and content loss in terms of the information differences between image features and image pixel performance. Our network resolves the interconnections between the traditional method and the convolutional neural network (CNN) module and obtains the enhanced result via the scattering removal and feature enhancement. It is beneficial to solve the dependence of feature learning on synthetic paired training data and the instability of semi-supervision learning in realizing image translation. Abstract features representations is also improved through definite direction and fine granularity related feature learning method. The traditional image enhancement module is optimized to make the best defogged result via parameters adjusting. The feature enhancement module is trained with adaptive moment estimation(ADAM) optimizer for 250 epochs with the momentum takes the values of 0.5 and 0.999. The learning rate is set to 2E-4. And unpaired 270×170 patches randomly cropped from 2 000 defogged real-world images and 2 000 clearance images are taken as the inputs of the generator and discriminator. The train and test processes are carried out by PyTorch in a X86 computer with a core i7 3.0 GHz processor

64 GB RAM and a NVIDIA 2080ti graphic.

Result

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Our experimental results are compared with 5 state-of-the-art enhancement methods

including the 2 traditional approaches and 3 deep learning methods on 2 public fog ground truth image datasets called RTTS(real-world task-driven testing set) and foggy driving dense dataset. RTTS dataset contains 4 322 foggy or dim images and foggy driving dense dataset has 21 dense fog online images collection. The quantitative evaluation metrics contain the image quality index and the detection quality index. The quality indexes are composed of the enhanced gradient ratio R and the blind image quality analyzer integrated local natural image quality evaluator(IL-NIQE). The detection indexes are based on mean average precision(MAP) and recall. We also demonstrated more enhanced results of each method for qualitative comparison in the experimental section. In RTTS and foggy driving dense dataset

compared with the method ranking second in each index

the mean R value is improved 50.83%

the mean IL-NIQE value is improved 6.33%

the MAP value is improved 6.40% and the mean Recall value is improved 7.79%. The enhanced results from the proposed method are much similar to clear color

brightness and contrast images qualitatively. The obtained experimental results illustrates that our network can improves the visual quality and machine perception for the foggy image captured in bad weather conditions with more than 50 (frame/s)/Mp as well.

Conclusion

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Our semi-supervision prior hybrid network integrates traditional restoration methods and deep learning based enhancement models for multi-level feature enhancement

achieving the enhancement of NSS features and TCS features. Our illustrations demonstrates our method has its priority for real foggy images in terms of image quality and object detectable ability for intelligent detection system.