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Single-image haze removal is an essential preprocessing phase in many object detection and segmentation approaches. Recently, end-to-end deep learning-based approaches have dominated the field of single-image dehazing because of their superiority in recovering clear images corrupted by different types of degradation. However, training an effective dehazing network remains challenging, particularly in the absence of high-quality realistic training datasets. In this paper, a novel approach called a subspace-based dehazing generative adversarial network (SuDGAN) is proposed. Traditional training methods attempt to apply changes to pixel intensities, whereas SuDGAN adopts a novel training approach using existing synthetic datasets to learn the adjustment of subspace components related to haze. This approach enables the network to learn more discriminative haze-aware features and focus on adjusting the components that are more affected by haze (luminance) while preserving those that are less influenced by haze (structure). The proposed SuDGAN, along with several state-of-the-art approaches, is evaluated on various challenging synthetic and realistic datasets using haze-related and traditional evaluation metrics. The experimental results demonstrate the efficiency of SuDGAN in removing haze and producing visually pleasing results. Furthermore, the results show that SuDGAN has clear quantitative and qualitative improvements over most state-of-the-art dehazing approaches. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the use of Generative Adversarial Networks (GANs). Thanks to their outstanding performance in image translation and generation, they play an increasingly important role in computer vision applications. Most approaches based on GAN focus on proposing task-specific auxiliary modules or loss functions that are tailored to address various challenges of a single application, but often do not perform better when evaluated to improve other image generation tasks. Moreover, the basic ResNet and U-Net based GAN generators reach their limits in many image restoration and enhancement use cases. Therefore, in this paper, we propose a generic GAN referred to as Multi-Kernel Filter-based Conditional Generative Adversarial Network (MFGAN). We develop a new GAN generator with multiple CNN streams to extract more relevant and discriminative features related to the studied task. The proposed MFNet generator consists of two CNN modules, feature extraction and feature compression, which are combined to connect both the GAN encoder and decoder. It considers the strengths of conventional layers at different scale levels with multi-kernel filtering to capture high to low feature frequencies that reflect the complex image degradations and structural image details. Extensive experiments on five challenging applications for image enhancement, image restoration, and infrared image translation demonstrate the superiority and effectiveness of the proposed MFGAN in removing image degradation and generating visually appealing fake images. Our MFGAN quantitatively outperforms both state-of-the-art GANs and other CNN-based architectures in all tested benchmarks. [ABSTRACT FROM AUTHOR]

Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has attracted much attention in recent decades due to the urgent need to develop biometric systems for many security applications. In this paper, Deep Writer Identification Network (DeepWINet), an effective deep Convolutional Neural Network (CNN) for writer identification, is proposed. The proposed model is evaluated in two different ways. In the first scenario, DeepWINet's CNN activation features, computed from the connected components of the writing, are passed to a customized nearest neighbor classifier for writer identification. In the second scenario, DeepWINet is evaluated as an end-to-end CNN network where the predicted results are averaged using an efficient strategy, Score Averaging Component-Decision Combiner. The proposed approach achieves competitive or the highest State-Of-The-Art performance on eight challenging handwritten databases with different languages. [ABSTRACT FROM AUTHOR]