Deep learning has become the mainstream approach for SAR image despeckling. However, the lack of speckle-free SAR images poses a significant challenge for supervised deep learning methods. To overcome this limitation, we propose a self-supervised SAR despeckling method that leverages a denoiser prior and attributed scattering centers to enhance the training process. Specifically, we use the output of an external denoiser as a pseudo-label for despeckling, while spatially correlated speckle noise in SAR images is decorrelated through random downsampling. The network is then updated by optimizing the similarity between its output and the pseudo-label. Additionally, an attributed scattering center map is introduced to help the network recognize strong scatterers and better preserve image details. Experiments on both synthetic and real SAR datasets demonstrate that our method outperforms existing despeckling approaches. Specifically, our method improves ENL by 16% over the recent self-supervised MERLIN framework on the TerraSAR scene. Meanwhile, our method achieves MOR and VOR values that are closer to the ideal value of 1.0. The code of our work is made freely available at https://github.com/Duan-NUDT/SARDIDP_ASC.

Deep learning techniques are extensively applied to synthetic aperture radar (SAR) ship detection tasks. Nonetheless, the limited availability of labeled SAR images impedes the neural network’s ability to learn and extract robust object features from SAR ship images. To alleviate the dependence on large datasets, this letter introduces a despeckle-based representation learning approach for SAR ship detection, named despeckling ship detection YOLO (DS-YOLO). The DS-YOLO model integrates a shared feature extractor, a detection head, and a despeckling head, facilitating the concurrent performance of SAR image despeckling and ship detection. The model effectively reduces the potential for neural network overfitting by conducting joint learning of detection and despeckling processes. As a result, DS-YOLO is particularly well-suited for SAR ship detection tasks with constrained training data. Comprehensive experiments indicate that DS-YOLO substantially surpasses the performance of the conventional detection model, particularly in scenarios where labeled data are severely restricted. Source codes are available athttps://github.com/Cthanta/DS-YOLO.

Deep learning-driven synthetic aperture radar automatic target recognition (SAR ATR) has gained increasing attention recently. However, current methods remain highly vulnerable to adversarial attacks, limiting their practical application. Most adversarial defense methods rely on adversarial training or attack detection, which tend to overfit and result in poor robustness against different attack types. Moreover, these methods show limited resilience to query-based attacks, where attackers iteratively probe the model to identify vulnerabilities and generate precise adversarial samples. To overcome these challenges, we propose a random response-based SAR purification defense (RRPD) framework composed of two key components: a diffusion purification module (DPM) and a multiexpert randomized response module (MRRM). The DPM removes adversarial noise through diffusion denoising while preserving critical information for accurate recognition. The MRRM introduces multiple expert models to increase response randomness, thus reducing the effectiveness of query-based attacks. Experimental results show that the proposed framework significantly enhances robustness against adversarial attacks on public SAR datasets, improving system security and reliability. The code is available athttps://github.com/SmartDSP2024/RRPD.

Multi-focus image fusion aims to integrate clear segments from different partially focused images, creating an ‘all-in-focus’ composite. Due to the lack of ground-truth for multi-focus image fusion, supervised deep learning methods are deemed inappropriate for this task. In this paper, we present an unsupervised approach for multi-focus image fusion, named Fusion2Void. Fusion2Void ingeniously tackles the challenge of missing ground-truth by framing image inpainting as an auxiliary task. Specifically, Fusion2Void utilizes a fusion network to merge focused regions from multiple source images. Following the fusion process, image patches in the source images are randomly dropped to construct an additional image inpainting task. Subsequently, an image inpainting network uses the fused image as a guide to restore the missing content in the source images. The missing content in the source images includes both focused and defocused regions. Restoring focused image patches is significantly more challenging than restoring their defocused counterparts due to their inclusion of more high-frequency details. If the focused image patches are effectively restored, the repair of the defocused image patches becomes notably easier. Therefore, the image inpainting network implicitly compels the fused image to incorporate all focused content from the source images, as these can be utilized to restore the missing focused regions in the source images perfectly. Based on image inpainting, the fusion network generates ‘all-in-focus’ images in an unsupervised manner. Experiments on several synthetic and real-world datasets highlight Fusion2Void’s state-of-the-art performance relative to other methods.

Oriented object detection in synthetic aperture radar (SAR) images presents significant challenges due to the scarcity of labeled data. In contrast, acquiring labeled optical remote sensing images is considerably easier. This article proposes a domain adaptive oriented object detection (DAOOD) model, termed the pixel-instance information transfer-based model (PITM). PITM aims to transfer knowledge from optical to SAR domains, thereby reducing the dependency of oriented SAR object detection on labels. Given the pronounced domain disparity between optical and SAR images, the efficient migration of both visual content and rotating instances is incorporated to bridge the gap in their information distribution simultaneously. Specifically, regarding pixel-level information transfer, speckle noise from SAR images is mixed into the optical domain to form an intermediate domain, thus compensating for the visual difference between the two domains. For instance-level information transfer, considering the angle diversity of rotating objects, multiscale and multidirectional spatial information extraction is combined with decoupled instance-invariant features, enhancing the cross-domain discernment capacity of rotating instances. Experimental results on four DAOOD benchmarks (i.e., two optical datasets to two SAR datasets) demonstrate that the proposed PITM significantly improves oriented object detection performance, even in the absence of labeled SAR images. Specifically, it individually outperforms two source-only models by 88.16% and 54.62% in average precision (AP).

Pan-sharpening is a task that aims to super-resolve the low-resolution multispectral (LRMS) image with the guidance of a corresponding high-resolution panchromatic (PAN) image. The key challenge in pan-sharpening is to accurately modeling the relationship between the MS and PAN images. While supervised deep learning methods are commonly employed to address this task, the unavailability of ground-truth severely limits their effectiveness. In this paper, we propose a mutually guided detail restoration method for unsupervised pan-sharpening. Specifically, we treat pan-sharpening as a blind image deblurring task, in which the blur kernel can be estimated by a CNN. Constrained by the blur kernel, the pan-sharpened image retains spectral information consistent with the LRMS image. Once the pan-sharpened image is obtained, the PAN image is blurred using a pre-defined blur operator. The pan-sharpened image, in turn, is used to guide the detail restoration of the blurred PAN image. By leveraging the mutual guidance between MS and PAN images, the pan-sharpening network can implicitly learn the spatial relationship between the two modalities. Extensive experiments show that the proposed method significantly outperforms existing unsupervised pan-sharpening methods.

We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as an Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We apply IDDP to various denoising tasks using only observed corrupted data and show that it achieves better denoising results than other self-supervised denoising methods.

Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since speckle-free SAR images are inaccessible, supervised denoising methods are not suitable for this task. To exploit the strong capabilities of convolutional neural networks (CNNs), we propose Unpaired Speckle Extraction (SAR-USE), an unsupervised method for SAR despeckling. Our method utilizes unpaired SAR and clean optical images to extract “real” speckle for learning despeckling. First, a CNN that has never seen clean SAR images is employed to extract speckle from the SAR image. Then, the extracted speckle is multiplied with a random optical image to synthesize paired data for learning speckle removal. Through a Siamese network, speckle extraction and learning despeckling are performed alternately and promote each other. To make the extracted speckle more visually and statistically realistic, it is constrained by a noise correction module to be unit mean while maintaining spatial correlation. After convergence, the CNN is a good denoiser that can effectively extract speckle from SAR images. Experiments on synthetic datasets show that the denoising ability of the proposed method is as good as its supervised counterpart. More importantly, SAR-USE is very efficient for removing the spatially correlated speckle in real data that supervised learning methods cannot.

Optimization algorithms are of great importance to efficiently and effectively train a deep neural network. However, the existing optimization algorithms show unsatisfactory convergence behavior, either slowly converging or not seeking to avoid bad local optima. Learning rate dropout (LRD) is a new gradient descent technique to motivate faster convergence and better generalization. LRD aids the optimizer to actively explore in the parameter space by randomly dropping some learning rates (to 0); at each iteration, only parameters whose learning rate is not 0 are updated. Since LRD reduces the number of parameters to be updated for each iteration, the convergence becomes easier. For parameters that are not updated, their gradients are accumulated (e.g., momentum) by the optimizer for the next update. Accumulating multiple gradients at fixed parameter positions gives the optimizer more energy to escape from the saddle point and bad local optima. Experiments show that LRD is surprisingly effective in accelerating training while preventing overfitting.

Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. Specifically, USUIR first estimates three latent components of the raw underwater image, i.e., the global background light, the transmission map, and the scene radiance (the clean image). Then, a re-degraded image is generated by randomly mixing up the estimated scene radiance and the raw underwater image. We demonstrate that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration. Extensive experiments show that USUIR achieves promising performance in both inference time and restoration quality.

Speckle removal is an important preprocessing step for synthetic aperture radar (SAR) imaging. Since speckle-free SAR images do not exist, supervised methods are not applicable. In this letter, we propose implicit deep denoiser prior (SAR-IDDP), a self-supervised method for SAR despeckling. SAR-IDDP uses a deep image prior (DIP) implicitly captured by the convolutional neural network (CNN) to formulate regularization instead of traditional hand-crafted priors. Specifically, we treat the output of the CNN as a “prior” that we denoise again after “renoising.” The CNN is updated to maximize the similarity between the again denoised image and its prior. The renoising procedure is designed based on the assumption of unit mean noise, while the spatial correlation of speckle is also involved. The despeckling ability of our method stems from CNN’s natural tendency to capture low-level image statistics. Experiments show that SAR-IDDP achieves significant improvements over existing model-based and self-supervised despeckling methods on both synthetic and real SAR images.

In many image denoising tasks, the difficulty of collecting noisy/clean image pairs limits the application of supervised CNNs. We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. To ease the interference of the image background, we use a noise removal module to aid noise extraction. The noise removal module first roughly removes noise from the noisy image, which is equivalent to excluding much background information. A noise approximation module can therefore easily extract a new noise map from the removed noise to match the gradient of the noisy input. This noise map is added to a random clean image to synthesize a new data pair, which is then fed back to the noise removal module to correct the noise removal process. These two modules cooperate to extract noise finely. After convergence, the noise removal module can remove noise without damaging other background details, so we use it as our final denoising network. Experiments show that the denoising performance of the proposed method is competitive with other supervised CNNs.

We present a weakly-supervised technique for learning to remove rain from images without using synthetic rain software. The method is based on a two-stage data distillation approach, which requires only some unpaired rainy and clean images to generate supervision. First, a rainy image is paired with a coarsely derained version using on a simple filtering technique (“rain-to-clean”). Then a clean image is randomly matched with the rainy soft-labeled pair. Through a shared deep neural network, the rain that is removed from the first image is then added to the clean image to generate a second pair (“clean-to-rain”). The neural network simultaneously learns to map both images such that high resolution structure in the clean images can inform the deraining of the rainy images. Demonstrations show that this approach can address those visual characteristics of rain not easily synthesized by software in the usual way.