Satellite Image Super-Resolution Using Generative Adversarial Networks (GANs)

Introduction: The advancement of artificial intelligence (AI) in medicine has exposed critical challenges, particularly the shortage of high-quality medical data and associated ethical concerns. Frontotemporal dementia (FTD) is a group of neurodegenerative conditions where early diagnosis is crucial. However, the development of AI-assisted diagnostic systems faces challenges due to the scarcity of labeled magnetic resonance imaging (MRI) data. Objective: To develop, compare, and evaluate two Generative Adversarial Network (GAN) models, Deep Convolutional GAN (DCGAN) and Progressive Growing GAN (PGGAN), for generating synthetic brain MRI slices, assessing image-level similarity and visual plausibility. Methods: We trained both models on a limited dataset of FTD brain MRIs. Our evaluation encompassed quantitative and qualitative assessments. We used three metrics for quantitative analysis: Structural Similarity Index (SSIM), Normalized Mutual Information (NMI), and Peak Signal-to-Noise Ratio (PSNR). Results: Both models generated synthetic MRIs. SSIM values were similar (≈0.215) with no practical difference; PGGAN surpassed DCGAN in NMI and PSNR (0.976 versus 0.798; 14.900 versus 12.426). Qualitative analysis corroborated these findings, with PGGAN producing images featuring enhanced detail. Conclusion: This research contributes to the field of synthetic medical image generation, offering a potential solution to data scarcity while maintaining patient privacy. The observed performance of PGGAN holds promise for augmenting limited datasets in neurological research. Furthermore, this study lays the groundwork for developing robust AI-assisted diagnostic tools in neurology, particularly for conditions like FTD, where extensive, diverse datasets are crucial yet challenging. Keywords: Artificial intelligence; frontotemporal dementia; magnetic resonance imaging; deep learning (Source: MeSH NLM)

High-quality medical imaging is essential for accurate diagnosis and effective treatment planning. However, limitations such as noise, artifacts, high acquisition costs, and patient safety concerns often restrict the ability to obtain high-resolution (HR) scans. Super-resolution (SR) techniques offer a promising alternative by reconstructing HR images from low-resolution (LR) inputs, thereby enhancing image quality without additional scanning. This capability is particularly important in clinical contexts, where improving image resolution directly impacts diagnostic confidence and therapeutic decision-making. In this work, we propose a hybrid SR framework that integrates transfer learning with a generative adversarial network (RESRGAN) and low-rank total variation (LRTV) regularization. This combination leverages both local neighborhood features and global contextual information to recover fine structural details while preserving overall image fidelity. The approach was evaluated on three brain imaging modalities – magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound – using volumetric datasets acquired at different time intervals. Quantitative experiments show that the proposed method consistently outperforms bicubic interpolation, achieving an average peak signal-to-noise ratio (PSNR) of 40.05 dB and a structural similarity index (SSIM) of 0.9808. Notably, ultrasound images exhibited the most substantial improvement. These findings highlight the potential of the proposed framework as a robust tool for enhancing medical image quality and supporting more reliable clinical decision-making.

The accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value.

Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topological features and automatically partitions the input graph into representative stages for feature learning. The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures. Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages (unlike a single GAN, which cannot easily identify such features, let alone reconstruct the original graph). This paper is firstline research on combining the use of GANs and graph topological analysis.

This chapter examines the use of Generative Adversarial Networks (GANs) in analyzing electroencephalogram (EEG) data. EEG is an electrophysiological method that records brain activity. EEG is used to diagnose neurological disorders and is also very important for brain-computer interface (BCI) systems. Although EEG data processing and analysis is widely used, it faces some difficulties, which reveals the necessity of advanced signal processing techniques. GANs, on the other hand, are advanced machine learning techniques and play an essential role in EEG data analysis. GANs are known for their ability to produce synthetic data similar to actual data, and this feature provides significant advantages in the analysis of EEG data. In particular, GANs are effective at filtering noise, improving data quality, and generating synthetic data. Given the complexity and diversity of EEG data, caution must be exercised in training GAN models and the accuracy of synthetic data. Current limitations of GANs in EEG data analysis and ongoing research to overcome these limitations are also examined.

Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative adversarial networks (GANs) have shown great promise in regularizing some of these image reconstruction problems. In this work, we apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct high fidelity images from incomplete medical imaging data. It is observed that the IAGAN method can potentially recover fine structures in the object that are relevant for medical diagnosis but may be oversmoothed in reconstructions with traditional sparsity-promoting regularization.

Traditional deblurring algorithms such as blur kernel estimation and gradient prior methods, these methods can only deal with a single type of blurry image, and such methods are very time-consuming. Due to the excellent performance of deep learning neural networks in the field of image generation, this paper regards the traditional image deblurring problem as a special image transformation problem. First, a multi-scale network is used to restore the blurry image, the network can restore the image in an end-to-end manner. Then, the multi-scale network will output a restored image. Finally, the generative adversarial network (GAN) can make the restored image closer to the real clear image in the sense. In terms of loss function, perceptual loss in content loss is adopted to ensure the consistency of the generated image and clear image in content. The results of deblurring will be evaluated by testing the peak signal to noise ratio (PSNR) and structural similarity (SSIM) between the generated image and the clear image. The results show that the PSNR and SSIM of the algorithm in the GoPro dataset have reached 28.01 and 0.8538, respectively, and can recover some details lost due to shake blur.

The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic is generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed. However, current applications of MCMC techniques have been limited to several object models such as a lumpy object model and a binary texture model, and it remains unclear how MCMC methods can be implemented with other more sophisticated object models. Deep learning methods that employ generative adversarial networks (GANs) hold great promise to learn stochastic object models (SOMs) from image data. In this study, we described a method to approximate the IO by applying MCMC techniques to SOMs learned by use of GANs. The proposed method can be employed with arbitrary object models that can be learned by use of GANs, thereby the domain of applicability of MCMC techniques for approximating the IO performance is extended. In this study, both signal-known-exactly (SKE) and signal-known-statistically (SKS) binary signal detection tasks are considered. The IO performance computed by the proposed method is compared to that computed by the conventional MCMC method. The advantages of the proposed method are discussed.

In the automatic sorting process of express delivery, a three-segment code is used to represent a specific area assigned by a specific delivery person. In the process of obtaining the courier order information, the camera is affected by factors such as light, noise, and subject shake, which will cause the information on the courier order to be blurred, and some information will be lost. Therefore, this paper proposes an image text deblurring method based on a generative adversarial network. The model of the algorithm consists of two generative adversarial networks, combined with Wasserstein distance, using a combination of adversarial loss and perceptual loss on unpaired datasets to train the network model to restore the captured blurred images into clear and natural image. Compared with the traditional method, the advantage of this method is that the loss function between the input and output images can be calculated indirectly through the positive and negative generative adversarial networks. The Wasserstein distance can achieve a more stable training process and a more realistic generation effect. The constraints of adversarial loss and perceptual loss make the model capable of training on unpaired datasets. The experimental results on the GOPRO test dataset and the self-built unpaired dataset showed that the two indicators, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), increased by 13.3% and 3%, respectively. The human perception test results demonstrated that the algorithm proposed in this paper was better than the traditional blur algorithm as the deblurring effect was better.

The underwater environment presents unique challenges (color distortions, reduced contrast, blurriness) hindering accurate analysis. This work introduces MuLA-GAN, a novel approach leveraging Generative Adversarial Networks (GANs) and specifically adapted Multi-Level Attention for comprehensive underwater image enhancement. MuLA-GAN integrates Multi-Level Attention within the GAN architecture to prioritize learning discriminative features crucial for precise image restoration. These relevant features encompass information on local details within image regions leveraged by spatial attention and features at various scales across the entire image captured by multi-level attention. This allows MuLA-GAN to identify and enhance objects, textures, and edges obscured by underwater distortions while also reconstructing a more accurate and visually clear representation of the underwater scene by analyzing low-level information like edges and textures, as well as high-level information like object shapes and global scene information. By selectively focusing on these relevant features, MuLA-GAN excels at capturing and preserving intricate details in underwater imagery, which is essential for various marine research, exploration, and resource management applications. Extensive evaluations on diverse datasets (UIEB test, UIEB challenge, U45, UCCS) demonstrate MuLA-GAN's superior performance compared to existing methods. Additionally, a specialized bio-fouling and aquaculture dataset confirms the model's robustness in challenging environments. On the UIEB test dataset, MuLA-GAN achieves exceptional Peak Signal-to-Noise Ratio (PSNR) (25.59) and Structural Similarity Index (SSIM) (0.893) scores, surpassing Water-Net (24.36 PSNR, 0.885 SSIM). This work addresses a significant research gap in underwater image enhancement by demonstrating the effectiveness of combining GANs with specifically adapted Multi-Level Attention mechanisms. This tailored approach offers a novel and comprehensive framework for restoring underwater image quality, providing valuable insights for accurate underwater scene analysis. The source code for MuLA-GAN is publicly available on GitHub at https://github.com/AhsanBaidar/MuLA_GAN.git

A common preprocessing step in vision-based applications is image dehazing, which has drawbacks such color distortion and detail loss. Conventional techniques frequently fall short of sufficiently retrieving sharp images from foggy situations. In order to improve single-image dehazing, this paper presents a unique method that combines Self-Attention Generative Adversarial Networks (SAGANs) with Cycle-Consistent Generative Adversarial Networks (CycleGAN). In order to capture long-range dependencies and enhance the perceptual quality of the dehazed images, our method includes self-attention processes from SAGANs and makes use of CycleGAN's strengths for efficient image-to-image translation without the need for paired training samples. We present a dual-framework design in which SAGANs improve textural and contextual details by focusing on relevant characteristics throughout the whole image space, while CycleGAN assures content fidelity through cycle consistency. We show through our studies that this integrated strategy achieves higher scores in common metrics like Peak Signal-to-Noise Ratio (PSNR) of 34.146 dB and Structural Similarity Index (SSIM) of 0.964, greatly outperforming both qualitative and quantitative assessments of previous methods. Together with establishing a new standard for single-image dehazing, this work shows how effective hybrid generative models can be when handling challenging image processing problems.

Underwater images often suffer from low contrast, color distortion, and noise due to light absorption and scattering in water. These issues make it challenging for marine applications such as underwater exploration, object detection, and autonomous navigation. Traditional image enhancement techniques struggle to recover natural colors and details effectively. This research proposes a U-Net-based Generative Adversarial Network (U-Net GAN) to enhance underwater images, focusing on preserving structural details while improving visual quality. The U-Net architecture ensures effective feature extraction and segmentation, while the GAN framework enhances image realism by learning high-level transformations. The model is trained and tested on benchmark datasets such as UIEB and EUVP, with performance evaluated using PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and UIQM (Underwater Image Quality Measure). Experimental results demonstrate that the proposed approach significantly enhances underwater images compared to conventional methods, restoring natural colors and improving visibility. This study contributes to advancing deep learning techniques for real-time underwater image enhancement, benefiting various marine research and surveillance applications.

Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks

Background: Reducing radiation dose in Computed Tomography (CT) is a clinical imperative, yet current low-dose CT (LDCT) protocols are inherently limited by noise and artifacts that obscure critical diagnostic details. While traditional iterative reconstruction and standard deep learning models have been proposed, they often introduce undesirable "waxy" textures and loss of fine structural edges. Methods: This study presents a high-fidelity image enhancement framework based on Generative Adversarial Networks (GANs) specifically optimized for LDCT denoising. Our approach integrates a multi-scale U-Net generator with a discriminator that evaluates both global structure and local texture. To ensure clinical reliability, we utilize a composite loss function comprising pixel-wise MSE, adversarial loss, and VGG-based perceptual loss, which encourages the model to preserve biologically relevant high-frequency details. Results: Experimental evaluation on the AAPM-Mayo Clinic dataset demonstrates that our proposed model significantly outperforms state-of-the-art methods, achieving a Peak Signal-to-Noise Ratio (PSNR) of 32.56 dB and a Structural Similarity Index (SSIM) of 0.912. Qualitative assessments confirm that our framework effectively suppresses quantum noise while maintaining the natural stochastic texture of CT images, facilitating better visualization of subtle lesions compared to standard CNN-based denoising. Conclusion: The proposed GAN-based framework bridges the gap between dose reduction and image quality, providing a viable pathway for high-quality diagnostic imaging at a fraction of the standard radiation exposure. This method holds significant potential for routine clinical use, particularly in screening scenarios where repeated exposure is a concern.

Pseudo-random number generators (PRNG) are a fundamental element of many security algorithms. We introduce a novel approach to their implementation, by proposing the use of generative adversarial networks (GAN) to train a neural network to behave as a PRNG. Furthermore, we showcase a number of interesting modifications to the standard GAN architecture. The most significant is partially concealing the output of the GAN’s generator, and training the adversary to discover a mapping from the overt part to the concealed part. The generator therefore learns to produce values the adversary cannot predict, rather than to approximate an explicit reference distribution. We demonstrate that a GAN can effectively train even a small feed-forward fully connected neural network to produce pseudo-random number sequences with good statistical properties. At best, subjected to the NIST test suite, the trained generator passed around 99% of test instances and 98% of overall tests, outperforming a number of standard non-cryptographic PRNGs.