Segmentation of roads in remote sensing images is a challenging task due to the inhomogeneous intensity, non-consistent contrast, and very cluttered background in remote sensing images. Recent approaches, mostly relying on convolutions or self-attention, make it difficult to extract weak and continuous road objects. Fourier neural operators provide another novel mechanism for capturing long-range and fine-grained features beyond self-attention. Based on it, we propose an adaptive Fourier convolution network (AFCNet) on the spatial-spectral domain for road segmentation in this paper. The AFCNet is built on the pipeline of the classical U-Net model and its core is the proposed Fourier neural encoder (FNE), which is built on a feed-forward layer and a flexible Fourier convolutional structure composed of Fourier-domain pooling layers, asymmetric convolutions, squeeze-excitation inspired self-attention and adaptive multiscale fusion layers. Furthermore, we combine the FNE and bottleneck in ResNet to form a hybrid global-local feature representation scheme to capture the long and weak road objects in remote sensing images. The experiments on two public datasets, the Massachusetts Roads and DeepGlobe Road Datasets, have shown that AFCNet worked with fewer parameters and outperformed most previous methods in terms of accuracy, precision, recall, and mean intersection over union (mIoU), etc.

Road segmentation from remote sensing images is a challenging task in capturing weak, long, and irregular road features due to the limited connectivity-preserving modeling capability. In this work, we proposed a U-shaped Fourier-deformable convolution network (FDNet) for road segmentation, which integrates the merits of deformable convolutions (DCs) and Fourier convolutions compactly. Specifically, a saliency-aware DC (SD-Conv) layer is proposed for tracing salient road features based on an iterative dynamic offset learning mechanism to grasp extremely tender and weak road objects. Meanwhile, a lightweight global feature extracting module based on spectral convolutions, namely, the adaptive Fourier convolution (AF-Conv) layer, is adopted to learn long-range dependency to extract long and continuous road structures. The proposed SD-Conv layer worked in parallel with the AF-Conv layer to construct a basic and compact block to build the U-shaped FDNet model for road segmentation. Furthermore, to maintain the continuity of road objects in complex road conditions, we introduced a topology-oriented loss function based on the Hausdorff distance (HD) on the persistence diagram (PD) of segmented results, and further combined with softDice loss components for fully supervised training. Our FDNet has been trained and evaluated on two benchmarks, and experimental results show that FDNet achieved state-of-the-art (SOTA) performance. Specifically, it achieved 80.34% on accuracy, 88.42% on precision, and 84.70% on mean intersection over union (mIoU), respectively, on the Massachusetts dataset, and achieved 99.05% on accuracy, 89.21% on precision, 88.61% on recall, and 81.37% on mIoU, respectively, on the DeepGlobe dataset, outperforming most previous methods on both datasets. Codes are available at:https://github.com/zhoucharming/FDNet.

Atmospheric turbulence can often introduce phase errors into a propagating light field, thus resulting in anisoplanatic and temporally varying blur and distortion of images. Restoring such images degraded by atmospheric turbulence is extremely ill-posed, due to multiple plausible solutions for a given input image. Most methods offer a deterministic estimation of clean images and require high-computational costs. To address these challenges, this article proposes a fast turbulence mitigation network (FTMNet). It is a lightweight model for atmospheric turbulence mitigation. Differing other methods, it does not employ a strategy for producing a single deterministic reconstruction. Instead, it leverages the Monte Carlo method to enhance restoration performance and produces a different and reasonable set of reconstructed images for a given input. As a result, FTMNet effectively mitigates atmospheric turbulence effect while maintaining low-inference time and computational resource requirements. Experimental results demonstrate that FTMNet shows high-inference speed, reaching 90 fps, and outperforms the state-of-the-art peers.

Abstract It is a key point to find out the actual privileges of network users in network security risk assessment. The Privilege dependency graph (PDG) provides an effective way to reason the actual privileges of network users from their initial privileges. The existing User Actual Privilege reasoning method is time‐consuming and not suitable for large‐scale networks. This paper introduces a fast User Actual Privilege reasoning framework based on PDG reduction. The core idea is to reduce the size of the graph as much as possible before the actual privilege reasoning. Three different nodes merged scenarios are proposed and discussed, as well as the influences of different execution sequences and execution times. Networks of different sizes were simulated to validate the effectiveness and scalability of their method. The experimental results show that the proposed method can decrease the time of User Actual Privilege reasoning by over 25% in large‐scale networks.

Most super-resolution methods are trained on datasets where high-resolution images and corresponding low-resolution images are obtained by the fixed degradation method. However, these external-based methods would fail to recover the detailed information of the test images if it could not be found in datasets. In this paper, we propose the momentum feature comparison network to generate the super-resolution image with rich texture information. Without the participation of high-resolution images in the training process, our method belongs to the completely unsupervised super-resolution method. A siamese structure with momentum update in generator is proposed to expand the content information of the low-resolution image and maintains the continuous consistency of intermediate feature maps. Furthermore, the results of two branches are fused through the feature fusion module to retain the global distribution of features and enhance local high-frequency details. The experimental results show that our method achieves great results compared with the state-of-the-art methods.

Different from image inpainting, image outpainting has relatively less context in the image center to capture and more content at the image border to predict. Therefore, classical encoder-decoder pipeline of existing methods may not predict the outstretched unknown content perfectly. In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed. Specifically, in two stages, a novel border sensitive convolution named adaptive filling convolution is designed for allowing encoder to predict the unknown content, alleviating the burden of decoder. Besides, to introduce prior knowledge to network and reinforce the inferring ability of encoder, siamese adversarial mechanism is designed to enable our network to model the distribution of covered long range feature as that of uncovered image feature. The results on four datasets has demonstrated that our method outperforms existing state-of-the-arts and could produce realistic results. Our code is released on https://github.com/nanjingxiaobawang/SieNet-Image-extrapolation.

Blind image deblurring is an important topic which is widely used in many research fields such as photography, optics, astronomy, medical images, monitoring, military and so on. Although many algorithms have been proposed to improve the deblurring result in the past years, most of them cannot perform perfectly in some challenging cases. This study presents a novel blind deblurring method based on an adaptive weighted total variation (TV) algorithm. The blur kernel estimation is based on the image structure, the sparsity and continuity prior of point spread function is also taken into account. To get better effect of removing the ringing artefacts, adaptive weight calculated according to the property of the higher‐order partial derivatives in the local image is proposed in TV algorithm to alleviate the ill‐posed inverse problem and stabilise the solution for latent image restoration. The experimental results prove that the proposed algorithm can suppress the ringing artefacts to a great extent in the latent image, and can get much better effect in both vision and theoretical results than traditional algorithms.