Under the nighttime haze environment, the quality of acquired images will be deteriorated significantly owing to the influences of multiple adverse degradation factors. In this paper, we develop a multi-purpose oriented haze removal framework focusing on nighttime hazy images. First, we construct a nonlinear model based on the classic Retinex theory to formulate multiple adverse degradations of a nighttime hazy image. Then, a novel variational Retinex model is presented to simultaneously estimate a smoothed illumination component and a detail-revealed reflectance component and predict the noise map from a pre-processed nighttime hazy image in a unified manner. Specifically, an ℓ ₀ norm is imposed on the reflectance to reveal the structural details and we make use of ℓ ₁ norm to constrain the piece-wise smoothness of the illumination and apply ℓ ₂ norm to enforce the total intensity of the noise map. Afterwards, the haze in the illumination component is removed based on prior-based dehazing method and the contrast of the reflectance component is improved in the gradient domain. Finally, we combine the dehazed illumination and the improved reflectance to generate the haze-free image. Experiments show that our proposed framework performs better than famous nighttime image dehazing methods both in visual effects and objective comparisons. In addition, the proposed framework can also be applicable to other types of degraded images.

Exhaled breath analysis by nanosensors is a workable and rapid manner to diagnose lung cancer in the early stage. In this paper, we proposed Al-doped MoSe₂ (Al–MoSe₂) as a promising biosensor for sensing three typically exhaled volatile organic compounds (VOCs) of lung cancer, namely, C₃H₄O, C₃H₆O, and C₅H₈, using the density functional theory (DFT) method. Single Al atom is doped on the Se-vacancy site of the MoSe₂ surface, which behaves as an electron-donor and enhances the electrical conductivity of the nanosystem. The adsorption and desorption performances, electronic behavior, and the thermostability of the Al–MoSe₂ monolayer are conducted to fully understand its physicochemical properties as a sensing material. The results indicate that the Al–MoSe₂ monolayer shows admirable sensing performances with C₃H₄O, C₃H₆O, and C₅H₈ with responses of −85.7, −95.6, and −96.3%, respectively. Also, the desirable adsorption performance and the thermostability endow with the Al–MoSe₂ monolayer with good sensing and desorbing behaviors for the recycle detection of three VOCs. We are hopeful that the results in this paper could provide some guidance to the experimentalists fulfilling their exploration in the practical application, which can also broaden the exploration of transition-metal dichalcogenides (TMDs) in more fields as well.

Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blur, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may lead to unsatisfactory restoration results. In this paper, we propose an end-to-end transformer-based framework for nighttime haze removal, called NightHazeFormer. Our proposed approach consists of two stages: supervised pre-training and semi-supervised fine-tuning. During the pre-training stage, we introduce two powerful priors into the transformer decoder to generate the non-learnable prior queries, which guide the model to extract specific degradations. For the fine-tuning, we combine the generated pseudo ground truths with input real-world nighttime hazy images as paired images and feed into the synthetic domain to fine-tune the pre-trained model. This semi-supervised fine-tuning paradigm helps improve the generalization to real domain. In addition, we also propose a large-scale synthetic dataset called UNREAL-NH, to simulate the real-world nighttime haze scenarios comprehensively. Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.

The visibility of images plays a crucial role in Intelligent Transportation Systems (ITS). However, images captured under hazy environments can degrade visual quality, significantly reducing the working performance of ITS. Although existing dehazing methods have achieved remarkable performance for daytime hazy images, they struggle to overcome the unique degradations under nighttime haze conditions such as glows, weak illumination, hidden noise, and color distortions. To simultaneously address these degradations, we propose VNDHR, a novel Variational Nighttime Dehazing framework using Hybrid Regularization focusing on enhancing the perceptual visibility of nighttime hazy scenarios. Specifically, a new physical model that accounts for multiple degradations under nighttime haze conditions is first constructed. Then, a novel hybrid variational model comprising an ℓ_p norm, a weighted ℓ₂ norm, and a total variation regularization is developed to obtain a structure-aware illumination and a noise-free reflectance, simultaneously. To remove the nonhomogeneous haze in the illumination, we employ the dark channel prior to estimate parameters in each grid patch. Furthermore, a simple but effective nonlinear stretching function is designed to enhance the texture in the decomposed reflectance component. Finally, the dehazed illumination and the stretched reflectance are combined to generate a haze-free result. Experiments performed on synthetic and real-world nighttime hazy images prove that our VNDHR framework achieves state-of-the-art dehazing performance, providing results with clear details and less noise. Besides, our VNDHR can also handle various types of degraded images well, such as low-light images, daytime hazy images, sandstorm images, and underwater images.