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Convolutional neural networks (CNNs) have performed well in image denoising. Although some CNNs enlarge convolutional kernels and increase stacked convolutional layers to overcome the locality defect of convolutional operations, they may increase computational costs. In this paper, we propose a parallel and serial denoising network (PSDNet) for image denoising to preserve image texture. Specifically, the proposed PSDNet contains a parallel block (PB), a serial block (SB), and a reconstruction block (RB). A PB uses two heterogeneous sub-networks with a deformable convolution in a parallel way to extract comparative information for better-recovering image texture. A SB utilizes an enhanced residual dense architecture via combinations of a batch normalization, ReLU, and convolutional layer in a serial way to refine obtained features for obtaining more accurate noise information. A RB is responsible for reconstructing images. Experimental results reveal that our PSDNet is very effective in image denoising, according to quantitative analysis and visual analysis. Codes can be obtained at https://github.com/hellloxiaotian/PSDNet. • Heterogeneous architecture with deformable convolution can better filter noise. • An enhanced residual architecture is used to remove redundant features. • Combining a parallel and serial way can improve effects of images denoising. • Proposed network is effective for synthesized and real noisy image denoising. [ABSTRACT FROM AUTHOR]

Remote sensing image denoising tasks are challenged by complex noise distributions and multiple noise types, including a mixture of additive Gaussian white noise (AWGN) and impulse noise (IN). For better image recovery, complex contextual information needs to be balanced while maintaining spatial details. In this paper, a denoising model based on multilevel progressive image recovery is proposed to address the problem of remote sensing image denoising. In our model, the deformable convolution improves spatial feature sampling to effectively capture image details. Meanwhile, attention-guided filtering is used to pass the output images from the first and second stages to the third stage in order to prevent information loss and optimize the image recovery effect. The experimental results show that under the mixed noise scene of Gaussian and pepper noise, our proposed model shows superior performance relative to several existing methods in terms of both visual effect and objective evaluation indexes. Our model can effectively reduce the influence of image noise and recover more realistic image details. [ABSTRACT FROM AUTHOR]

Automatic pavement crack detection is a critical technique in the intelligent transportation system, which can effectively replace the person with a machine to detect the pavement crack automatically. This task is excellently challenging due to the tiny texture and the various shapes of each crack object. Previous crack detection networks mainly aim to perform complicated multi-scale feature fusion to learn the semantic information of cracks. However, the typical symmetrical networks with high-to-low resolution are undesirable to extract detailed crack texture information, and these existing methods cannot effectively deal with the issue of the various shapes.This paper proposes a novel end-to-end U-shaped convolutional neural network, termed CrackUNet, for the pavement crack detection task. To extract the information of the tiny texture, we design a novel network-in-network structure, which can enlarge the receptive field and obtain multi-scale features by putting a sub-network into each convolutional layer. Besides, to handle the issue of the various shapes for the crack object, we exploit the deformable convolution to capture contextual information of each crack, which can further improve the performance of crack detection. We train and evaluate the proposed CrackUNet on three public pavement crack datasets. The quantitative experimental results illustrate that our network outperforms the current state-of-the-art methods with almost the same efficiency. Specifically, the precision, recall, and F1-score of the CrackUNet are approximately 92.27%, 93.99%, and 92.94%, respectively. [ABSTRACT FROM AUTHOR]

To solve the tracking accuracy degradation problem in scenarios with large displacements or nonrigid motion during target tracking, this paper proposes an optical flow estimation method based on multiscale anisotropic convolution. The network structure is improved in a step-by-step manner by extracting the data flow from the network according to the observed features. For the low-level neural network, a layered multiscale structure is used to build a cascade network by using hybrid dilated convolution to obtain feature information at different scales while ensuring the tracking accuracy. For the upper-layer neural network, hybrid inflated deformable convolution is used by learning the contextual long-range correlations and multidirectional adaptive offsets of features. Experiments are conducted on the Flying Chairs, KITTI, and MPI datasets. The results show that compared with various popular algorithm methods, the model in this paper reduces endpoint errors while retaining edge information in regions with large displacements or nonrigid motion. Code is available at https://github.com/yifanna/MACFlow-pytorch. [ABSTRACT FROM AUTHOR]

Pavement disease detection is an important task for ensuring road safety. Manual visual detection requires a significant amount of time and effort. Therefore, an automated road disease identification technique is required to guarantee that city tasks are performed. However, due to the irregular shape and large-scale differences in road diseases, as well as the imbalance between the foreground and background, the task is challenging. Because of this, we created the deep convolution neural network—DASNet, which can be used to identify road diseases automatically. The network employs deformable convolution instead of regular convolution as the feature pyramid's input, adds the same supervision signal to the multi-scale features before feature fusion, decreases the semantic difference, extracts context information by residual feature enhancement, and reduces the information loss of the pyramid's top-level feature map. Considering the unique shape of road diseases, imbalance problems between the foreground and background are common, therefore, we introduce the sample weighted loss function. In order to prove the superiority and effectiveness of this method, it is compared to the latest method. A large number of experiments show that this method is superior in accuracy to other methods, specifically, under the COCO evaluation metric, compared with the Faster RCNN baseline, the proposed method obtains a 41.1 mAP and 3.4 AP improvement. [ABSTRACT FROM AUTHOR]