According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods canno...
Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacture...
Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popul...
Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the...
Fluorescence imaging is an important and efficient tool in cell biology and biomedical research. In order to observe the dynamics of biological macromolecules such as DNA, RNA and proteins in live cells, it is extremely necessary to surpass the Abbe...
Single-image dehazing is an ill-posed problem that has attracted a myriad of research efforts. However, virtually all methods proposed thus far assume that input images are already affected by haze. Little effort has been spent on autonomous single-i...
SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors b...
Single image super-resolution (SISR) aims to recover a high-resolution image from a single low-resolution image. In recent years, SISR methods based on deep convolutional neural networks have achieved remarkable success, and some methods further impr...
Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current atte...
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a...
High-energy particles in space often induce single event effects in CMOS image sensors, resulting in performance degradation and functional failure. This paper focuses on the formation and morphology of transient bright spots in CMOS image sensors an...
Single-pixel imaging (SPI), generally based on computational imaging, has the advantages of a wide bandwidth and imaging objects beyond the visual field. However, for general SPI, to obtain a picture with few shadows by one photodetector is a great c...
The dynamic range of an X-ray digital imaging system is very important when detecting objects with a high absorption ratio. In this paper, a ray source filter is used to filter the low-energy ray components which have no penetrating power to the high...
To reconstruct the wavefront in a single-lens coherent diffraction imaging (CDI) system, we propose a closed-loop cascaded iterative engine (CIE) algorithm based on the known information of the imaging planes. The precision of diffraction distance is...
Recently, supervised deep super-resolution (SR) networks have achieved great success in both accuracy and texture generation. However, most methods train in the dataset with a fixed kernel (such as bicubic) between high-resolution images and their lo...
In recent years, numerous single-image dehazing algorithms have made significant progress; however, dehazing still presents a challenge, particularly in complex real-world scenarios. In fact, single-image dehazing is an inherently ill-posed problem,...
In the last decade, the vision systems have improved their capabilities to capture 3D images in bad weather scenarios. Currently, there exist several techniques for image acquisition in foggy or rainy scenarios that use infrared (IR) sensors. Due to...
Existing camera calibration methods using a single image have exhibited some limitations. These limitations include relying on large datasets, using inconveniently prepared calibration objects instead of commonly used planar patterns such as checkerb...
Single-pixel imaging (SPI) is a promising imaging scheme based on compressive sensing. However, its application in high-resolution and real-time scenarios is a great challenge due to the long sampling and reconstruction required. The Deep Learning Co...
Rain streaks degrade the quality of optical images and seriously affect the effectiveness of subsequent vision-based algorithms. Although the applications of a convolutional neural network (CNN) and self-attention mechanism (SA) in single image derai...
Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortu...
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/...
Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easil...
Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on de...
We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neur...
This paper examines methods to best exploit the High Dynamic Range (HDR) of the single photon avalanche diode (SPAD) in a high fill-factor HDR photon counting pixel that is scalable to megapixel arrays. The proposed method combines multi-exposure HDR...
Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However,...
Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details...
High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing...
Intraoperative guidance using targeted fluorescent tracers can potentially provide surgeons with real-time feedback on the presence of tumor tissue in resection margins. To overcome the limited depth penetration of fluorescent light, combining fluore...
In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems,...
This paper introduces SIDe-HBIM (Single-Image Depth inference for HBIM), a semi-automated image-to-BIM pipeline aimed at improving the integration of architectural decorative elements into HBIM environments. The research addresses the difficulty of r...
Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for th...
Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map...
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representi...
Historical perspective images have been proved to be very useful to properly provide a dimensional analysis of buildings façades or even to generate a pseudo-3D reconstruction based on rectified images of the whole structure. In this paper, the case...
With the development of deep learning, considerable progress has been made in image restoration. Notably, many state-of-the-art single image super-resolution (SR) methods have been proposed. However, most of them contain many parameters, which leads...
Image encryption is a confidential strategy to keep the information in digital images from being leaked. Due to excellent chaotic dynamic behavior, self-feedbacked Hopfield networks have been used to design image ciphers. However, Self-feedbacked Hop...
The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To f...
Turbulence-degraded image frames are distorted by both turbulent deformations and space–time varying blurs. Restoration of the atmospheric turbulence-degraded image is of great importance in the state of affairs, such as remoting sensing, surve...
The existing single image super-resolution (SISR) methods that consider integer scale factors (X2, X3, X4, and X8), have been developed well, but SISR methods with arbitrary scale factors (X1.3, X2.5, and X3.7) have gradually gained attention recentl...
The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an opt...
Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification mod...
Single-pixel imaging (SPI) has attracted increasing attention in recent years because of its advantages in imaging systems. However, a low reconstruction quality and a long reconstruction time have hindered the development of SPI. Hence, in this stud...
Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (S...
Under the existing single sample condition, the fatigue detection method of an automobile driver has some problems, such as an improper camera calibration method, image denoising beyond the controllable range, low fatigue detection accuracy, and unsa...
In remote sensing, object classification often suffers from severe degradation caused by atmospheric turbulence and low-signal conditions. Traditional image reconstruction approaches are computationally expensive and fragile under such conditions. In...
Precise and quick measurement of samples’ flow velocities is essential for cell sorting timing control and reconstruction of acquired image-analyzed data. We developed a simple technique for the single-shot measurement of flow velocities of particles...
From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser n...
Due to the requirement of video surveillance, machine learning-based single image deraining has become a research hotspot in recent years. In order to efficiently obtain rain removal images that contain more detailed information, this paper proposed...
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