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Domain generalizable person re-identification (DG ReID) aims to obtain a model that can be applied directly to unseen domains once trained on a set of source domains (datasets collected from different camera networks). Among current DG ReID methods, instance normalization is a promising solution to reduce the effect of domain bias, but it inevitably filters out some discriminative information. Besides, since most pioneering approaches cannot effectively address the loss of discriminative information, this paper proposes a Style Elimination and Information Restitution (SEIR) module and constructs a generalizable framework. Specifically, we utilize instance normalization to reinforce the generalization capability on unseen domains. Then, the instance-specific mean and variance are employed to construct a feature vector that includes discriminative and style information. Besides, an encoder–decoder structure is designed to restitute the discriminative information to ensure a high recognition rate. Finally, a dual optimization strategy is devised to update the proposed module by simulating a real train-test process to enhance the generalization robustness further and prevent SEIR from overfitting to the source domain. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin on the public DG ReID benchmarks. • A Style Elimination and Information Restitution module for handling domain bias. • An optimization strategy for improving the generalization ability. • We propose a framework for generalizable person re-identification. • Significant results were achieved for addressing the generalizable person re-identification. [ABSTRACT FROM AUTHOR]

In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques. [ABSTRACT FROM AUTHOR]

In social networks, people usually communicate online by posting and passing images, people will find that there are many similar or even visually identical images. When the images determine whether the published information is true or false, original images became crucial, and at the same time, deep learning excelled in image identification. Combining the original image identification and deep learning methods, this paper proposes the original image identification method based on the deep network models, aiming to use the deep network models to identify the original images between the original images and a bunch of variant images of the original images, and obtain satisfactory results. In this way, through the trained network models, the original images can be identified and can help the viewers to determine the correctness of the information. Experimental results verify the feasibility, effectiveness and security of the proposed method. • The method can prevent the images from being stolen during transmission. • The method can achieve good identification results on the image databases. • The method is feasible and flexible in actual operation. [ABSTRACT FROM AUTHOR]

• An image fusion method is proposed that can effectively highlight infrared target features and retain visible visual information. • Infrared targets are analyzed based on the saliency weight map. • The target contour and background texture are refined by a method based on weight refinement of guided filtering. • A multi-layer fusion strategy is used to fuse saliency weight maps at multiple scales. Image fusion is the fusion of multiple images from the same scene to produce a more informative image, and infrared and visible image fusion is an important branch of image fusion. To tackle the issues of diminished luminosity in the infrared target, inconspicuous target features, and blurred texture of the fused image after the fusion of infrared and visible images. This paper introduces a novel effective fusion framework that merges multi-scale Convolutional Neural Networks (CNN) with saliency weight maps. First, the method measures the source image features to estimate the initial saliency weight map. Then, the initial weight map is segmented and optimized using a guided filter before being further processed by CNN. Next, a trained Siamese convolutional network is used to solve the two key problems of activity measure and weight assignment. Meanwhile, a multi-layer fusion strategy is designed to effectively retain the luminance of the infrared target and the texture information in the visible background. Finally, adaptive adjustment of the fusion coefficients is achieved by employing saliency. The experimental results show that the method outperforms the state-of-the-art algorithms in terms of both subjective visual quality and objective evaluation effects. [ABSTRACT FROM AUTHOR]

Previous deep learning-based single image dehazing methods are implemented by using real-valued convolutional neural network (RV-CNN), which ignore the phase information of the image, and tend to cause models to perform well on synthetic datasets but low generalization on real-world datasets. In this paper, we propose a novel Complex-Valued convolutional Dehazing Network (CVD-Net), which considers both amplitude and phase information of image for haze removal in real-world scenes. Specifically, we construct complex-valued transformation module, including complex-valued convolutional layer and residual block embedded into the middle of the network to extract features of image efficiently, and also design a complex-valued selected fusion module (CVSF) and complex-valued attention module (CVAM) to promote the interaction between different scale features for preserving image detailed information. Both qualitative and quantitative experimental results show that the proposed CVD-Net can effectively remove the haze, and has good generalization in real-world hazy images. [ABSTRACT FROM AUTHOR]