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Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing multi-stage methods fail to effectively inpainting the larger missing areas, their common drawback is that the result of each stage is easily misguided by the wrong content generated in the previous stage. To solve this issue, in this paper, a novel one-stage generative adversarial network based on the progressive decoding architecture and gradient guidance. Firstly, gradient priors are extracted at the encoder stage to be passed to the decoding branch, and multiscale attention fusion group is used to help the network understand the image features. Secondly, multiple parallel decoding branches fill and refine the missing regions by top-down passing the reconstructed priors. This progressively guided repair avoids the detrimental effects of inappropriate priors. The joint guidance of features and gradient priors helps the restoration results contain the correct structure and rich details. And the progressive guidance is achieved by our fusion strategy, combining reimage convolution and design channel coordinate attention to fuse and reweight the features of different branches. Finally, we use the multiscale fusion to merge the feature maps at different scales reconstructed by the last decoding branch and map them to the image space, which further improves the semantic plausibility of the restoration results. Experiments on multiple datasets show that the qualitative and quantitative results of our computationally efficient model are competitive with those of state-of-the-art methods.

In the underwater domain, small object detection plays a crucial role in the protection, management, and monitoring of the environment and marine life. Advancements in deep learning have led to the development of many efficient detection techniques. However, the complexity of the underwater environment, limited information available from small objects, and constrained computational resources make small object detection challenging. To tackle these challenges, this paper presents an efficient deep convolutional network model. First, a CSP for small object and lightweight (CSPSL) module is introduced to enhance feature retention and preserve essential details. Next, a variable kernel convolution (VKConv) is proposed to dynamically adjust the convolution kernel size, enabling better multi-scale feature extraction. Finally, a spatial pyramid pooling for multi-scale (SPPFMS) method is presented to preserve the features of small objects more effectively. Ablation experiments on the UDD dataset demonstrate the effectiveness of the proposed methods. Comparative experiments on the UDD and DUO datasets demonstrate that the proposed model delivers the best performance in terms of computational cost and detection accuracy, outperforming state-of-the-art methods in real-time underwater small object detection tasks.

With the advances in technology, humans tend to explore the world underwater in a more constructive way than before. The appearance of an underwater object varies depending on depth, biological composition, temperature, ocean currents, and other factors. This results in colour distorted images and hazy images with low contrast. To address the aforesaid problems, in proposed approach, initially White balance algorithm is carried out to pre-process original underwater image. Contrast enhanced image is achieved by applying the Contrast Limited Adaptive Histogram Equalization algorithm (CLAHE). In CLAHE, tile size and clip limit are the major parameters that control the enhanced image quality. Hence, to enhance the contrast of images optimally, Firefly algorithm is adopted for CLAHE. Dark Channel Prior algorithm (DCP) is modified with guided filter correction to get the sharpened version of the underwater image. Multiscale fusion strategy was performed to fuse CLAHE enhanced and dehazed images. Finally, the restored image is treated with optimal CLAHE to improve visibility of enhanced underwater image. Experimentation is carried out on different underwater image datasets such as U45 and RUIE and resulted in UIQM = 5.1384, UCIQE = 0.6895 and UIQM = 5.4875, UCIQE = 0.6953 respectively which shows the superiority of proposed approach.

This paper propose a new fault identification method based on variable pooling multiscale CNN (VPMCNN), which solves the bearing industrial problem of huge variable features and inherent multiscale characteristics in acoustic emission (AE) signals. First, the pooling projection components (PPCs) of the signals are obtained through the variable pooling layer. The PPCs consider the curse of invariant feature weight in traditional CNN pooling layer, and select the more weighted features to enhance the classifying quality. Second, an improved multiscale fusion feature module is introduced to further extract the hidden features, which is called fused components (FCs). The FCs aims to automatically extract multiple scale features using different filter sizes from raw acoustic signals. Then the GAP (Global Average Pooling) layer is performed to realize classification. Finally, the fault identification using the proposed algorithm is performed by testing the bearing AE signals with single operating condition and variable operating conditions, and the results show the effectiveness of the proposed method, compared with existing AE based bearing fault identification methods.

Facial micro-expression (ME), consisting of uncontrollable muscle movements in faces, is an important clue for revealing real people’s feelings. Due to the short duration and low intensity, the salient feature representation learning is the main challenge for robust facial ME recognition. To acquire the diverse and spatial relation representation, this paper proposes a simple and yet distinctive micro-expression recognition model based on multiscale convolutional fusion and multi-channel capsule network (MCFMCN). Firstly, the apex frame in a ME clip, located by computing the pixel difference between frames, is filtered by the optical flow transformation. Secondly, a multiscale fusion module is introduced to capture diverse ME related details. Then, to further explore the subtle spatial relations between parts in the ME faces, the multi-channel capsule network is designed to improve the feature representation performance of the traditional single channel capsule network. Finally, the entire ME recognition model is trained and verified on three benchmarks (CASMEII, SAMM, and SMIC) using the associated standard evaluation protocols: unweighted average recall rate (UAR) and unweighted F1 score (UF1). ME recognition experiments indicate that our method based on MCFMCN can improve the UAR (from 75.79% to 83.58%) and UF1(from79.37% to 87.06%) in comparison with the traditional capsule network. Extensive experimental results show the performance of proposed ME recognition is superior to that of works based on pervious single channel capsule network or other state-of-the-art CNN models, which validates the finding that combination of multi-scale analysis and multi-channel capsule network is feasible and effective to improve the ME recognition performance.

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder–decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation.

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

Hyperspectral image (HSI) classification is a basic and significant task in remote sensing, the aim of which is to assign a class label to each pixel in an image. Recently, deep learning networks have been widely applied in HSI classification. They can extract discriminative spectral–spatial features through spectral weighting and multiscale spatial information modeling. However, existing spectral weighting mechanisms lack the ability to explore the inter-class spectral overlap caused by spectral variability. Moreover, current multiscale fusion strategies ignore semantic conflicts between features with large-scale differences. To address these problems, a class-aware progressive multiscale fusion transformer (CPMFFormer) is proposed. It first introduces class information into a spectral weighting mechanism. This helps CPMFFormer to learn class-specific spectral weights and enhance class-discriminative spectral features. Then, a center residual convolution module is constructed to extract features at different scales. It is embedded with a center feature calibration layer to achieve hierarchical enhancement of representative spatial features. Finally, a progressive multiscale fusion strategy is designed to promote effective collaboration between features at different scales. It achieves a smooth semantic transition by gradually fusing adjacent scale features. Experiments using five public HSI datasets show that CPMFFormer is rational and effective.

The underwater optical imaging environment presents unique challenges due to its complexity. This paper addresses the limitations of existing algorithms in handling underwater images captured in artificial light scenes. We proposed an underwater artificial light optimization algorithm to preprocess images with uneven lighting, mitigating the effects of light distortion. Furthermore, we proposed a novel underwater image enhancement algorithm based the Multiscale Fusion Generative Adversarial Network, named UMSGAN, to address the issues of low contrast and color distortion. UMSGAN uses the generative adversarial network as the underlying framework and first extracts information from the degraded image through three parallel branches separately, and adds residual dense blocks in each branch to learn deeper features. Subsequently, the features extracted from the three branches are fused and the detailed information of the image is recovered by the reconstruction module, named RM. Finally, multiple loss functions are linearly superimposed, and the adversarial network is trained iteratively to obtain the enhanced underwater images. The algorithm is designed to accommodate various underwater scenes, providing both color correction and detail enhancement. We conducted a comprehensive evaluation of the proposed algorithm, considering both qualitative and quantitative aspects. The experimental results demonstrate the effectiveness of our approach on a diverse underwater image dataset. The proposed algorithm exhibits superior performance in terms of enhancing underwater image quality, achieving significant improvements in contrast, color accuracy, and detail preservation. The proposed methodology exhibits promising results, offering potential applications in various domains such as underwater photography, marine exploration, and underwater surveillance.

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.