Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Occluder-aided non-line-of-sight imaging

Non-line-of-sight (NLOS) imaging is the inference of the properties of objects or scenes outside of the direct line-of-sight of the observer. Such inferences can range from a 2D photograph-like image of a hidden area, to determining the position, motion or number of hidden objects, to 3D reconstructions of a hidden volume. NLOS imaging has many enticing potential applications, such as leveraging the existing hardware in many automobiles to identify hidden pedestrians, vehicles or other hazards and hence plan safer trajectories. Other potential application areas include improving navigation for robots or drones by anticipating occluded hazards, peering past obstructions in medical settings, or in surveying unreachable areas in search-and-rescue operations. Most modern NLOS imaging methods fall into one of two categories: active imaging methods that have some control of the illumination of the hidden area, and passive methods that simply measure light that already exists. This thesis introduces two NLOS imaging methods, one of each category, along with modeling and data processing techniques that are more broadly applicable. The methods are linked by their use of objects (‘occluders’) that reside somewhere between the observer and the hidden scene and block some possible light paths. Computational periscopy, a passive method, can recover the unknown position of an occluding object in the hidden area and then recover an image of the hidden scene behind it. It does so using only a single photograph of a blank relay wall taken by an ordinary digital camera. We develop also a framework using an optimized preconditioning matrix to improve the speed at which these reconstructions can be made and greatly improve the robustness to ambient light. Lastly, we develop tools necessary to demonstrate recovery of scenes at multiple unknown depths – paving the way towards three-dimensional reconstructions. Edge-resolved transient imaging, an active method, enables the formation of 2.5D representations – a plan view plus heights – of large-scale scenes. A pulsed laser illuminates spots along a small semi-circle on the floor, centered on the edge of a vertical wall such as in a doorway. The wall edge occludes some light paths, only allowing the laser light reflecting off of the floor to illuminate certain portions of the hidden area beyond the wall, depending on where along the semi-circle it is illuminating. The time at which photons return following a laser pulse is recorded. The occluding wall edge provides angular resolution, and time-resolved sensing provides radial resolution. This novel acquisition strategy, along with a scene response model and reconstruction algorithm, allow for 180° field of view reconstructions of large-scale scenes unlike other active imaging methods. Lastly, we introduce a sparsity penalty named mutually exclusive group sparsity (MEGS), that can be used as a constraint or regularization in optimization problems to promote solutions in which certain components are mutually exclusive. We explore how this penalty relates to other similar penalties, develop fast algorithms to solve MEGS-regularized problems, and demonstrate how enforcing mutual exclusivity structure can provide great utility in NLOS imaging problems

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine