Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, convolutional neural network-based methods have dominated image dehazing. However, vision Transformers, which has recently made a breakthrough in high-level vision tasks, has not brought new dimensions to image dehazing. We start with the popular Swin Transformer and find that several of its key designs are unsuitable for image dehazing. To this end, we propose DehazeFormer, which consists of various improvements, such as the modified normalization layer, activation function, and spatial information aggregation scheme. We train multiple variants of DehazeFormer on various datasets to demonstrate its effectiveness. Specifically, on the most frequently used SOTS indoor set, our small model outperforms FFA-Net with only 25% #Param and 5% computational cost. To the best of our knowledge, our large model is the first method with the PSNR over 40 dB on the SOTS indoor set, dramatically outperforming the previous state-of-the-art methods. We also collect a large-scale realistic remote sensing dehazing dataset for evaluating the method’s capability to remove highly non-homogeneous haze. We share our code and dataset at https://github.com/IDKiro/DehazeFormer.

Conventional intrusion detection systems based on supervised learning techniques require a large number of samples for training, while in some scenarios, such as zero-day attacks, security agencies can only intercept a limited number of shots of malicious samples. Therefore, there is a need for few-shot detection. In this paper, a detection method based on a meta-learning framework is proposed for this purpose. The proposed method can be used to distinguish and compare a pair of network traffic samples as a basic task of learning, including a normal unaffected sample and a malicious one. To accomplish this task, we design a deep neural network (DNN) named FC-Net, which mainly comprises two parts: feature extraction network and comparison network. FC-Net learns a pair of feature maps for classification from a pair of network traffic samples, then compares the obtained feature maps, and finally determines whether the pair of samples belongs to the same type. To evaluate the proposed detection method, we construct two datasets for few-shot network intrusion detection based on real network traffic data sources, using a specifically developed approach. The experimental results indicate that the proposed detection method is universal and is not limited to specific datasets or attack types. Training and testing on the same datasets demonstrate that the proposed method can achieve the average detection rate up to 98.88%. The outcome of training on one dataset and testing on the other one confirms that the proposed method can achieve better performance. In a few-shot scenario, malicious samples in an untrained dataset can be detected successfully, and the average detection rate is up to 99.62%.

Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a uniform disk in previous work. In this paper, we propose that it can be more accurately depicted using the generalized Gaussian (GG) function. This is motivated by the theoretical analysis of the out-of-focus blur and the practical observation of real blur kernels. We show that as the out-of-focus blur kernels are of specific shapes, the GG function can be further simplified to a single-parameter model. We estimate the parameter of the GG blur kernel from image patches containing step edges, and obtain the clear image by non-blind image deblurring. Experimental results validate that the proposed GG blur kernel estimation algorithm outperforms the state-of-the-art ones deploying either parametric (disk and Gaussian) or nonparametric kernels, and consequently benefits the image deblurring process.

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environemntal information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

Deep convolutional neural networks have achieved great performance on various image restoration tasks. Specifically, the residual dense network (RDN) has achieved great results on image noise reduction by cascading multiple residual dense blocks (RDBs) to make full use of the hierarchical feature. However, the RDN only performs well in denoising on a single noise level, and the computational cost of the RDN increases significantly with the increase in the number of RDBs, and this only slightly improves the effect of denoising. To overcome this, we propose the dynamic residual dense network (DRDN), a dynamic network that can selectively skip some RDBs based on the noise amount of the input image. Moreover, the DRDN allows modifying the denoising strength to manually get the best outputs, which can make the network more effective for real-world denoising. Our proposed DRDN can perform better than the RDN and reduces the computational cost by $40–50\%$ . Furthermore, we surpass the state-of-the-art CBDNet by 1.34 dB on the real-world noise benchmark.

Deep learning-based methods have surpassed the traditional methods in image denoising due to the prior knowledge accumulated on the large dataset. Because of the difference between additive Gaussian white noise (AWGN) and real noise, researchers have recently paid more attention to real-world image denoising. Based on the characteristics of real-world image denoising, we revise the method of noise synthesis and design a novel network to make full use of the multi-scale context. Our proposed approach dramatically surpasses all the contemporary approaches on the sRGB track of the DND benchmark with PSNR over 40dB and SSIM over 0.96.