Infrared Image Super-Resolution: Systematic Review, and Future Trends

Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at \url{https://github.com/yongsongH/Infrared_Image_SR_SurveyComment: Submitted to IEEE TNNL

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Three-dimensional imaging with multiple degrees of freedom using data fusion

This paper presents an overview of research work and some novel strategies and results on using data fusion in 3-D imaging when using multiple information sources. We examine a variety of approaches and applications such as 3-D imaging integrated with polarimetric and multispectral imaging, low levels of photon flux for photon-counting 3-D imaging, and image fusion in both multiwavelength 3-D digital holography and 3-D integral imaging. Results demonstrate the benefits data fusion provides for different purposes, including visualization enhancement under different conditions, and 3-D reconstruction quality improvement

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging

Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this paper, we present a framework to understand the building blocks of this nascent field of end-to-end design of camera hardware and algorithms. As part of this framework, we show how methods that exploit both physics and data have become prevalent in imaging and computer vision, underscoring a key trend that will continue to dominate the future of task-specific camera design. Finally, we share current barriers to progress in end-to-end design, and hypothesize how these barriers can be overcome

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications