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Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective “U-Net in U-Net” framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at https://github.com/danfenghong/IEEE [ABSTRACT FROM AUTHOR]

Unsupervised image-to-image translation aims to learn the mapping from an input image in a source domain to an output image in a target domain without paired training dataset. Recently, remarkable progress has been made in translation due to the development of generative adversarial networks (GANs). However, existing methods suffer from the training instability as gradients passing from discriminator to generator become less informative when the source and target domains exhibit sufficiently large discrepancies in appearance or shape. To handle this challenging problem, in this paper, we propose a novel multi-constraint adversarial model (MCGAN) for image translation in which multiple adversarial constraints are applied at generator’s multi-scale outputs by a single discriminator to pass gradients to all the scales simultaneously and assist generator training for capturing large discrepancies in appearance between two domains. We further notice that the solution to regularize generator is helpful in stabilizing adversarial training, but results may have unreasonable structure or blurriness due to less context information flow from discriminator to generator. Therefore, we adopt dense combinations of the dilated convolutions at discriminator for supporting more information flow to generator. With extensive experiments on three public datasets, cat-to-dog, horse-to-zebra, and apple-to-orange, our method significantly improves state-of-the-arts on all datasets. [ABSTRACT FROM AUTHOR]

Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been demonstrated to fool the deep classification networks. There are two key characteristics of these attacks: firstly, these perturbations are mostly additive noises carefully crafted from the deep neural network itself. Secondly, the noises are added to the whole image, not considering them as the combination of multiple components from which they are made. Motivated by these observations, in this research, we first study the role of various image components and the impact of these components on the classification of the images. These manipulations do not require the knowledge of the networks and external noise to function effectively and hence have the potential to be one of the most practical options for real-world attacks. Based on the significance of the particular image components, we also propose a transferable adversarial attack against unseen deep networks. The proposed attack utilizes the projected gradient descent strategy to add the adversarial perturbation to the manipulated component image. The experiments are conducted on a wide range of networks and four databases including ImageNet and CIFAR-100. The experiments show that the proposed attack achieved better transferability and hence gives an upper hand to an attacker. On the ImageNet database, the success rate of the proposed attack is up to 88.5%, while the current state-of-the-art attack success rate on the database is 53.8%. We have further tested the resiliency of the attack against one of the most successful defenses namely adversarial training to measure its strength. The comparison with several challenging attacks shows that: (i) the proposed attack has a higher transferability rate against multiple unseen networks and (ii) it is hard to mitigate its impact. We claim that based on the understanding of the image components, the proposed research has been able to identify a newer adversarial attack unseen so far and unsolvable using the current defense mechanisms. [ABSTRACT FROM AUTHOR]

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is– rather than improving the segmentation quality compared to the baseline method– to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/). [ABSTRACT FROM AUTHOR]