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  • Open Access


    Adaptive Noise Detector and Partition Filter for Image Restoration

    Cong Lin1, Chenghao Qiu1, Can Wu1, Siling Feng1,*, Mengxing Huang1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249

    Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >

  • Open Access


    CLGA Net: Cross Layer Gated Attention Network for Image Dehazing

    Shengchun Wang1, Baoxuan Huang1, Tsz Ho Wong2, Jingui Huang1,*, Hong Deng1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4667-4684, 2023, DOI:10.32604/cmc.2023.031444

    Abstract In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor, combined with the channel attention mechanism, to better extract the restored features. A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index (SSIM), peak signal to noise ratio (PSNR) and subjective visual quality. In order to improve the efficiency of… More >

  • Open Access


    Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

    Kazim Ali1,*, Adnan N. Qureshi1, Ahmad Alauddin Bin Arifin2, Muhammad Shahid Bhatti3, Abid Sohail3, Rohail Hassan4

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.020111

    Abstract These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction.… More >

  • Open Access


    A New Method of Image Restoration Technology Based on WGAN

    Wei Fang1,2,*, Enming Gu1, Weinan Yi1, Weiqing Wang1, Victor S. Sheng3

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 689-698, 2022, DOI:10.32604/csse.2022.020176

    Abstract With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding technology having been adjusted and… More >

  • Open Access


    Deep Neural Network Driven Automated Underwater Object Detection

    Ajisha Mathias1, Samiappan Dhanalakshmi1,*, R. Kumar1, R. Narayanamoorthi2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168

    Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an… More >

  • Open Access


    Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

    Seonjae Kim, Dongsan Jun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651

    Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the… More >

  • Open Access


    FPD Net: Feature Pyramid DehazeNet

    Shengchun Wang1, Peiqi Chen1, Jingui Huang1,*, Tsz Ho Wong2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1167-1181, 2022, DOI:10.32604/csse.2022.018911

    Abstract We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and… More >

  • Open Access


    UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting

    Chung-Il Kim1, Jehyeok Rew2, Yongjang Cho2, Eenjun Hwang2,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3447-3463, 2021, DOI:10.32604/cmc.2021.017633

    Abstract Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling.… More >

  • Open Access


    Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

    Ghulfam Zahra1, Muhammad Imran1, Abdulrahman M. Qahtani2,*, Abdulmajeed Alsufyani2, Omar Almutiry3, Awais Mahmood3, Fayez Eid Alazemi4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3465-3481, 2021, DOI:10.32604/cmc.2021.017454

    Abstract In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer… More >

  • Open Access


    Motion-Blurred Image Restoration Based on Joint Invertibility of PSFs

    Yuye Zhang1,*, Jingli Huang1, Jiandong Liu1, Hakeel Ahmed Chohan2

    Computer Systems Science and Engineering, Vol.36, No.2, pp. 407-416, 2021, DOI:10.32604/csse.2021.014154

    Abstract To implement restoration in a single motion blurred image, the PSF (Point Spread Function) is difficult to estimate and the image deconvolution is ill-posed as a result that a good recovery effect cannot be obtained. Considering that several different PSFs can get joint invertibility to make restoration well-posed, we proposed a motion-blurred image restoration method based on joint invertibility of PSFs by means of computational photography. Firstly, we designed a set of observation device which composed by multiple cameras with the same parameters to shoot the moving target in the same field of view continuously to obtain the target images… More >

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