Image-to-Image Translation with Conditional Adversarial Networks

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.Comment: Website: https://phillipi.github.io/pix2pix/, CVPR 201

An Information-theoretic analysis of generative adversarial networks for image restoration in physics-based vision

Image restoration in physics-based vision (such as image denoising, dehazing, and deraining) are fundamental tasks in computer vision that attach great significance to the processing of visual data as well as subsequent applications in different fields. Existing methods mainly focus on exploring the physical properties and mechanisms of the imaging process, and tend to use a deconstructive idea in describing how the visual degradations (like noise, haze, and rain) are integrated with the background scenes. This idea, however, relies heavily on manually engineered features and handcrafted composition models, which can be theories only in ideal conditions or hypothetical models that may involve human bias or fail in simulating true situations in actual practices. With the progress of representation learning, generative methods, especially generative adversarial networks (GANs), are considered a more promising solution for image restoration tasks. It directly learns the restorations as end-to-end generation processes using large amounts of data without understanding their physical mechanisms, and it also allows completing missing details damaged information by involving external knowledge and generating plausible results with intelligent-level interpretation and semantics-level understanding of the input images. Nevertheless, existing studies that try to apply GAN models to image restoration tasks dose not achieve satisfactory performances compared with the traditional deconstructive methods. And there is scarcely any study or theory to explain how deep generative models work in relevant tasks. In this study, we analyzed the learning dynamics of different deep generative models based on the information bottleneck principle and propose an information-theoretic framework to explain the generative methods for image restoration tasks. In which, we study the information flow in the image restoration models and point out three sources of information involved in generating the restoration results: (i) high-level information extracted by the encoder network, (ii) low-level information from the source inputs that retained, or pass directed through the skip connections, and, (iii) external information introduced by the learned parameters of the decoder network during the generation process. Based on this theory, we pointed out that conventional GAN models may not be directly applicable to the tasks of image restoration, and we identify three key issues leading to their performance gaps in the image restoration tasks: (i) over-invested abstraction processes, (ii) inherent details loss, and (iii) imbalance optimization with vanishing gradient. We formulate these problems with corresponding theoretical analyses and provide empirical evidence to verify our hypotheses and prove the existence of these problems respectively. To address these problems, we then proposed solutions and suggestions including optimizing network structure, enhancing details extraction and accumulation with network modules, as well as replacing measures of training objectives, to improve the performances of GAN models on the image restoration tasks. Ultimately, we verify our solutions on bench-marking datasets and achieve significant improvement on the baseline models

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network

Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Improvements in Digital Holographic Microscopy

The Ph.D. dissertation consists of developing a series of innovative computational methods for improving digital holographic microscopy (DHM). DHM systems are widely used in quantitative phase imaging for studying micrometer-size biological and non-biological samples. As any imaging technique, DHM systems have limitations that reduce their applicability. Current limitations in DHM systems are: i) the number of holograms (more than three holograms) required in slightly off-axis DHM systems to reconstruct the object phase information without applying complex computational algorithms; ii) the lack of an automatic and robust computation algorithm to compensate for the interference angle and reconstruct the object phase information without phase distortions in off-axis DHM systems operating in telecentric and image plane conditions; iii) the necessity of an automatic computational algorithm to simultaneously compensate for the interference angle and numerically focus out-of-focus holograms on reconstructing the object phase information without phase distortions in off-axis DHM systems operating in telecentric regime; iv) the deficiency of reconstructing phase images without phase distortions at video-rate speed in off-axis DHM operating in telecentric regime, and image plane conditions; v) the lack of an open-source library for any DHM optical configuration; and, finally, vi) the tradeoff between speckle contrast and spatial resolution existing in current computational strategies to reduce the speckle contrast. This Ph.D. dissertation is motivated to overcome or at least reduce the six limitations mentioned above. Each chapter of this dissertation presents and discusses a novel computational method from the theoretical and experimental point of view to address each of these limitations

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis