We are delighted to present the anniversary Horizons in Imaging article collection. This series showcases high-impact, authoritative, and reader-friendly articles covering the most topical research at the forefront of imaging. All contributing authors were nominated by the Chief Editors of the Journal in recognition of their prominence and influence in their respective fields. The cutting-edge work presented in this article collection highlights the diversity of research performed across the entire breadth of the field and reflects on the latest advances in the theory and methodology with applications to compelling problems in academic and translational research. Contributions to the collection are by invitation only. New articles will be added to this collection as they are published.

Robert Gordon University

University of Florence

Aerospace Information Research Institute, Chinese Academy of Sciences (CAS)

Frontiers in Imaging

Image Capture

Image Retrieval and Analysis

Imaging Applications

Image Coding and Security

Horizons in Imaging

Editorial: Horizons in imaging

Monitoring the number of people in various spaces of a building is important for optimizing space usage, assisting with public safety, and saving energy. Diverse approaches have been developed for different end goals, from ID card readers for space management, to surveillance cameras for security, to CO2 sensing for HVAC control. In the last few years, fisheye cameras mounted overhead have become the sensing modality of choice because they offer large-area coverage and significantly-reduced occlusions but research efforts are still nascent. In this paper, we provide an overview of recent research efforts in this area and propose one new direction. First, we identify benefits and challenges related to inference from top-view fisheye images, and summarize key public datasets. Then, we review efforts in algorithm development for detecting people from a single fisheye frame and from a group of sequential frames. Finally, we focus on counting people indoors. While this is straightforward for a single camera, when multiple cameras are used to monitor a space, person re-identification is needed to avoid overcounting. We describe a framework for people counting using two cameras and demonstrate its effectiveness in a large classroom for location-based person re-identification. To support people counting in even larger spaces, we propose two new person re-identification algorithms using N > 2 overhead fisheye cameras. We provide ample experimental results throughout the paper.

Monitoring the number of people in various spaces of a building is important for optimizing space usage, assisting with public safety, and saving energy. Diverse approaches have been developed for different end goals, from ID card readers for space management, to surveillance cameras for security, to CO2 sensing for HVAC control. In the last few years, fisheye cameras mounted overhead have become the sensing modality of choice because they offer large-area coverage and significantly-reduced occlusions but research efforts are still nascent. In this paper, we provide an overview of recent research efforts in this area and propose one new direction. First, we identify benefits and challenges related to inference from top-view fisheye images, and summarize key public datasets. Then, we review efforts in algorithm development for detecting people from a single fisheye frame and from a group of sequential frames. Finally, we focus on counting people indoors. While this is straightforward for a single camera, when multiple cameras are used to monitor a space, person re-identification is needed to avoid overcounting. We describe a framework for people counting using two cameras and demonstrate its effectiveness in a large classroom for location-based person re-identification. To support people counting in even larger spaces, we propose two new person re-identification algorithms using N &gt; 2 overhead fisheye cameras. We provide ample experimental results throughout the paper.

Overhead fisheye cameras for indoor monitoring: challenges and recent progress

Quantitative tracking of rapidly moving micron-scale objects remains an elusive challenge in microscopy due to low signal-to-noise. This paper describes a novel method for tracking micron-sized motile organisms in off-axis Digital Holographic Microscope (DHM) raw holograms and/or reconstructions. We begin by processing the microscopic images with the previously reported Holographic Examination for Life-like Motility (HELM) software, which provides a variety of tracking outputs including motion history images (MHIs). MHIs are stills of videos where the frame-to-frame changes are indicated with color time-coding. This exposes tracks of objects that are difficult to identify in individual frames at a low signal-to-noise ratio. The visible tracks in the MHIs are superior to tracks identified by all tested automated tracking algorithms that start from object identification at the frame level, particularly in low signal-to-noise ratio data, but do not provide quantitative track data. In contrast to other tracking methods, like Kalman filter, where the recording is analyzed frame by frame, MHIs show the whole time span of particle movement at once and eliminate the need to identify objects in individual frames. This feature also enables post-tracking identification of low-SNR objects. We use these tracks, rather than object identification in individual frames, as a basis for quantitative tracking of Bacillus subtilis by first generating MHIs from X, Y, and t stacks (raw holograms or a projection over reconstructed planes), then using a region-tracking algorithm to identify and separate swimming pathways. Subsequently, we identify each object's Z plane of best focus at the corresponding X, Y, and t points, yielding ap full description of the swimming pathways in three spatial dimensions plus time. This approach offers an alternative to object-based tracking for processing large, low signal-to-noise datasets containing highly motile organisms.

Quantitative tracking of rapidly moving micron-scale objects remains an elusive challenge in microscopy due to low signal-to-noise. This paper describes a novel method for tracking micron-sized motile organisms in off-axis Digital Holographic Microscope (DHM) raw holograms and/or reconstructions. We begin by processing the microscopic images with the previously reported Holographic Examination for Life-like Motility (HELM) software, which provides a variety of tracking outputs including motion history images (MHIs). MHIs are stills of videos where the frame-to-frame changes are indicated with color time-coding. This exposes tracks of objects that are difficult to identify in individual frames at a low signal-to-noise ratio. The visible tracks in the MHIs are superior to tracks identified by all tested automated tracking algorithms that start from object identification at the frame level, particularly in low signal-to-noise ratio data, but do not provide quantitative track data. In contrast to other tracking methods, like Kalman filter, where the recording is analyzed frame by frame, MHIs show the whole time span of particle movement at once and eliminate the need to identify objects in individual frames. This feature also enables post-tracking identification of low-SNR objects. We use these tracks, rather than object identification in individual frames, as a basis for quantitative tracking of Bacillus subtilis by first generating MHIs from X, Y, and t stacks (raw holograms or a projection over reconstructed planes), then using a region-tracking algorithm to identify and separate swimming pathways. Subsequently, we identify each object&apos;s Z plane of best focus at the corresponding X, Y, and t points, yielding ap full description of the swimming pathways in three spatial dimensions plus time. This approach offers an alternative to object-based tracking for processing large, low signal-to-noise datasets containing highly motile organisms.

Motion history images: a new method for tracking microswimmers in 3D

Efficient and accurate radiology reporting is critical in modern healthcare for timely diagnosis and patient care. In this paper, we present a novel deep learning approach that leverages BioGPT and co-attention mechanisms for automatic chest X-ray report generation. Our model, termed “ChestBioX-Gen” is designed to bridge the gap between medical images and textual reports. BioGPT, a biological language model, contributes its contextual understanding to the task, while the co-attention mechanism efficiently aligns relevant regions of the image with textual descriptions. This collaborative combination enables ChestBioX-Gen to generate coherent and contextually accurate reports that embed complex medical findings. Our model not only reduces the burden on radiologists but also enhances the consistency and quality of reports. By automating the report generation process, ChestBioX-Gen contributes to faster diagnoses and improved patient care. Quantitative evaluations, measured through BLEU-N and Rouge-L metrics, demonstrate the model's proficiency in producing clinically relevant reports with scores of 0.6685, 0.6247, 0.5689, 0.4806, and 0.7742 on BLUE 1, 2, 3, 4, and Rouge-L, respectively. In conclusion, the integration of BioGPT and co-attention mechanisms in ChestBioX-Gen represents an advancement in AI-driven medical image analysis. As radiology reporting plays a critical role in healthcare, our model holds the potential to revolutionize how medical insights are extracted and communicated, ultimately benefiting both radiologists and patients.

Efficient and accurate radiology reporting is critical in modern healthcare for timely diagnosis and patient care. In this paper, we present a novel deep learning approach that leverages BioGPT and co-attention mechanisms for automatic chest X-ray report generation. Our model, termed &#x201c;ChestBioX-Gen&quot; is designed to bridge the gap between medical images and textual reports. BioGPT, a biological language model, contributes its contextual understanding to the task, while the co-attention mechanism efficiently aligns relevant regions of the image with textual descriptions. This collaborative combination enables ChestBioX-Gen to generate coherent and contextually accurate reports that embed complex medical findings. Our model not only reduces the burden on radiologists but also enhances the consistency and quality of reports. By automating the report generation process, ChestBioX-Gen contributes to faster diagnoses and improved patient care. Quantitative evaluations, measured through BLEU-N and Rouge-L metrics, demonstrate the model&apos;s proficiency in producing clinically relevant reports with scores of 0.6685, 0.6247, 0.5689, 0.4806, and 0.7742 on BLUE 1, 2, 3, 4, and Rouge-L, respectively. In conclusion, the integration of BioGPT and co-attention mechanisms in ChestBioX-Gen represents an advancement in AI-driven medical image analysis. As radiology reporting plays a critical role in healthcare, our model holds the potential to revolutionize how medical insights are extracted and communicated, ultimately benefiting both radiologists and patients.

ChestBioX-Gen: contextual biomedical report generation from chest X-ray images using BioGPT and co-attention mechanism

Computational imaging technology (CIT), with its many variations, addresses the limitations of industrial design. CIT can effectively overcome the bottlenecks in physical information acquisition, model development, and resolution by being tightly coupled with mathematical calculations and signal processing in information acquisition, transmission, and interpretation. Qualitative improvements are achieved in the dimensions, scale, and resolution of the information. Therefore, in this review, the concepts and meaning of CIT are summarized before establishing a real CIT system. The basic common problems and relevant challenging technologies are analyzed, particularly the non-linear imaging model. The five typical imaging requirements–distance, resolution, applicability, field of view, and system size–are detailed. The corresponding key issues of super-large-aperture imaging systems, imaging beyond the diffraction limit, bionic optics, interpretation of light field information, computational optical system design, and computational detectors are also discussed. This review provides a global perspective for researchers to promote technological developments and applications.

Computational imaging technology (CIT), with its many variations, addresses the limitations of industrial design. CIT can effectively overcome the bottlenecks in physical information acquisition, model development, and resolution by being tightly coupled with mathematical calculations and signal processing in information acquisition, transmission, and interpretation. Qualitative improvements are achieved in the dimensions, scale, and resolution of the information. Therefore, in this review, the concepts and meaning of CIT are summarized before establishing a real CIT system. The basic common problems and relevant challenging technologies are analyzed, particularly the non-linear imaging model. The five typical imaging requirements&#x2013;distance, resolution, applicability, field of view, and system size&#x2013;are detailed. The corresponding key issues of super-large-aperture imaging systems, imaging beyond the diffraction limit, bionic optics, interpretation of light field information, computational optical system design, and computational detectors are also discussed. This review provides a global perspective for researchers to promote technological developments and applications.

Computational optical imaging: challenges, opportunities, new trends, and emerging applications

IntroductionA low-cost, close-range photogrammetric surface scanner is proposed, made from Computer Numerical Control (CNC) components and an off-the-shelf, consumer-grade macro camera.</sec>MethodsTo achieve micrometer resolution in reconstruction, accurate and photorealistic surface digitization, and retain low manufacturing cost, an image acquisition approach and a reconstruction method are proposed. The image acquisition approach uses the CNC to systematically move the camera and acquire images in a grid tessellation and at multiple distances from the target surface. A relatively large number of images is required to cover the scanned surface. The reconstruction method tracks keypoint features to robustify correspondence matching and uses far-range images to anchor the accumulation of errors across a large number of images utilized.</sec>Results and discussionQualitative and quantitative evaluation demonstrate the efficacy and accuracy of this approach.</sec>

Introduction: A low-cost, close-range photogrammetric surface scanner is proposed, made from Computer Numerical Control (CNC) components and an off-the-shelf, consumer-grade macro camera.Methods: To achieve micrometer resolution in reconstruction, accurate and photorealistic surface digitization, and retain low manufacturing cost, an image acquisition approach and a reconstruction method are proposed. The image acquisition approach uses the CNC to systematically move the camera and acquire images in a grid tessellation and at multiple distances from the target surface. A relatively large number of images is required to cover the scanned surface. The reconstruction method tracks keypoint features to robustify correspondence matching and uses far-range images to anchor the accumulation of errors across a large number of images utilized.Results and discussion: Qualitative and quantitative evaluation demonstrate the efficacy and accuracy of this approach.