Showing papers on "Digital camera published in 2019"

Toward Real-World Single Image Super-Resolution: A New Benchmark and a New Model

Abstract: Most of the existing learning-based single image super-resolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i.e., bicubic downsampling) to their high-resolution (HR) counterparts. However, the degradations in real-world LR images are far more complicated. As a consequence, the SISR models trained on simulated data become less effective when applied to practical scenarios. In this paper, we build a real-world super-resolution (RealSR) dataset where paired LR-HR images on the same scene are captured by adjusting the focal length of a digital camera. An image registration algorithm is developed to progressively align the image pairs at different resolutions. Considering that the degradation kernels are naturally non-uniform in our dataset, we present a Laplacian pyramid based kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels to recover the HR image. Our extensive experiments demonstrate that SISR models trained on our RealSR dataset deliver better visual quality with sharper edges and finer textures on real-world scenes than those trained on simulated datasets. Though our RealSR dataset is built by using only two cameras (Canon 5D3 and Nikon D810), the trained model generalizes well to other camera devices such as Sony a7II and mobile phones.

Computational periscopy with an ordinary digital camera

Abstract: Computing the amounts of light arriving from different directions enables a diffusely reflecting surface to play the part of a mirror in a periscope-that is, perform non-line-of-sight imaging around an obstruction. Because computational periscopy has so far depended on light-travel distances being proportional to the times of flight, it has mostly been performed with expensive, specialized ultrafast optical systems1-12. Here we introduce a two-dimensional computational periscopy technique that requires only a single photograph captured with an ordinary digital camera. Our technique recovers the position of an opaque object and the scene behind (but not completely obscured by) the object, when both the object and scene are outside the line of sight of the camera, without requiring controlled or time-varying illumination. Such recovery is based on the visible penumbra of the opaque object having a linear dependence on the hidden scene that can be modelled through ray optics. Non-line-of-sight imaging using inexpensive, ubiquitous equipment may have considerable value in monitoring hazardous environments, navigation and detecting hidden adversaries.

Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model

Abstract: Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i.e., bicubic downsampling) to their high-resolution (HR) counterparts. However, the degradations in real-world LR images are far more complicated. As a consequence, the SISR models trained on simulated data become less effective when applied to practical scenarios. In this paper, we build a real-world super-resolution (RealSR) dataset where paired LR-HR images on the same scene are captured by adjusting the focal length of a digital camera. An image registration algorithm is developed to progressively align the image pairs at different resolutions. Considering that the degradation kernels are naturally non-uniform in our dataset, we present a Laplacian pyramid based kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels to recover the HR image. Our extensive experiments demonstrate that SISR models trained on our RealSR dataset deliver better visual quality with sharper edges and finer textures on real-world scenes than those trained on simulated datasets. Though our RealSR dataset is built by using only two cameras (Canon 5D3 and Nikon D810), the trained model generalizes well to other camera devices such as Sony a7II and mobile phones.

Depth From a Polarisation + RGB Stereo Pair

Abstract: In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shape-from-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.

Noise suppression by controlling the sparsity of the point spread function in interferenceless coded aperture correlation holography (I-COACH).

Abstract: Interferenceless coded aperture correlation holography (I-COACH) is an incoherent opto-digital technique for imaging 3D objects. In I-COACH, the light scattered from an object is modulated by a coded phase mask (CPM) and then recorded by a digital camera as an object digital hologram. To reconstruct the image, the object hologram is cross-correlated with the point spread function (PSF)-the intensity response to a point at the same object's axial location recorded with the same CPM. So far in I-COACH systems, the light from each object point has scattered over the whole camera area. Hence, the signal-to-noise ratio per camera pixel is lower in comparison to the direct imaging in which each point is imaged to a single image point. In this work, we consider the midway between the camera responses of a single point and of a continuous pattern over the entire camera area. The light in this study is focused onto a set of dots randomly distributed over the camera plane. With this technique, we show that there is a PSF with a best number of dots, yielding an image with a maximum product of the signal-to-noise ratio and the image visibility and a maximum value of structural similarity.

Velocity Estimation From a Single Linear Motion Blurred Image Using Discrete Cosine Transform

Abstract: There is a growing trend to use a digital camera as an instrument to measure velocity instead of a regular sensor approach. This paper introduces a new proposal for estimating kinematic quantities, namely, the angle and the relative speed, from a single motion blur image using the discrete cosine transform (DCT). Motion blur is a common phenomenon present in images due to the relative movement between the camera and the objects, during sensor exposure to light. Today, this source of kinematic data is mostly dismissed. The introduced technique focuses on cases where the camera moves at a constant linear velocity while the background remains unchanged. 2250 motion blur pictures were shot for the angle experiments and 500 for the speed estimation experiments, in a light and distance controlled environment, using a belt motor slider driven at angles between 0° and 90° and 10 preset speeds. The DCT Hough and DCT Radon results were compared to discrete Fourier transform (DFT) Hough and DFT Radon algorithms for angle estimation. The mean absolute error of the DCT Radon method for direction estimation was 4.66°. In addition, the mean relative error for speed estimation of the DCT Pseudocepstrum was 5.15%. The innovative DCT frequency analysis proposals were more accurate than all competitors evaluated for the reconstruction of the point spread function that enables calculation of relative velocity and motion direction. These results demonstrate that cameras as an instrument can be used to measure velocity even using a single linear motion blur degraded image.

Method for 3-D Scene Reconstruction Using Fused LiDAR and Imagery From a Texel Camera

Abstract: Reconstructing a 3-D scene from aerial sensor data creating a textured digital surface model (TDSM), consisting of a LiDAR point cloud and an overlaid image, is valuable in many applications including agriculture, military, surveying, and natural disaster response. When collecting LiDAR from an aircraft, the navigation system accuracy must exceed the LiDAR accuracy to properly reference returns in 3-D space. Precision navigation systems can be expensive and often require full-scale aircraft to house such systems. Synchronizing the LiDAR sensor and a camera, using a texel camera calibration, provides additional information that reduces the need for precision navigation equipment. This paper describes a bundle adjustment technique for aerial texel images that allows for relatively low-accuracy navigation systems to be used with low-cost LiDAR and camera data to form higher fidelity terrain models. The bundle adjustment objective function utilizes matching image points, measured LiDAR distances, and the texel camera calibration and does not require overlapping LiDAR scans or ground control points. The utility of this method is proven using a simulated texel camera and unmanned aerial system (UAS) flight data created from aerial photographs and elevation data. A small UAS is chosen as the target vehicle due to its relatively inexpensive hardware and operating costs, illustrating the power of this method in accurately referencing the LiDAR and camera data. In the 3-D reconstruction, the 1- $\sigma $ accuracy between LiDAR measurements across the scene is on the order of the digital camera pixel size.

Overview of open source software for close range photogrammetry

Abstract: . In the photogrammetric process of the 3D reconstruction of an object or a building, multi-image orientation is one of the most important tasks that often include simultaneous camera calibration. The accuracy of image orientation and camera calibration significantly affects the quality and accuracy of all subsequent photogrammetric processes, such as determining the spatial coordinates of individual points or 3D modeling. In the context of artificial vision, the full-field analysis procedure is used, which leads to the so-called Strcture from Motion (SfM), which includes the simultaneous determination of the camera's internal and external orientation parameters and the 3D model. The procedures were designed and developed by means of a photogrammetric system, but the greatest development and innovation of these procedures originated from the computer vision from the late 90s, together with the SfM method. The reconstructions on this method have been useful for visualization purposes and not for photogrammetry and mapping. Thanks to advances in computer technology and computer performance, a large number of images can be automatically oriented in a coordinate system arbitrarily defined by different algorithms, often available in open source software (VisualSFM, Bundler, PMVS2, CMVS, etc.) or in the form of Web services (Microsoft Photosynth, Autodesk 123D Catch, My3DScanner, etc.). However, it is important to obtain an assessment of the accuracy and reliability of these automated procedures. This paper presents the results obtained from the dome low close range photogrammetric surveys and processed with some open source software using the Structure from Motion approach: VisualSfM, OpenDroneMap (ODM) and Regard3D. Photogrammetric surveys have also been processed with the Photoscan commercial software by Agisoft. For the photogrammetric survey we used the digital camera Canon EOS M3 (24.2 Megapixel, pixel size 3.72 mm). We also surveyed the dome with the Faro Focus 3D TLS. Only one scan was carried out, from ground level, at a resolution setting of ¼ with 3x quality, corresponding to a resolution of 7 mm / 10 m. Both TLS point cloud and Photoscan point cloud were used as a reference to validate the point clouds coming from VisualSFM, OpenDroneMap and Regards3D. The validation was done using the Cloud Compare open source software.

A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification

Abstract: Vehicle-type classification is considered a core module for many intelligent transportation applications, such as speed monitoring, smart parking systems, and traffic analysis. In this paper, many vision-based classification techniques were presented relying only on a digital camera without the need for any extra hardware components. Dimension and color are two important characteristics of any digital image that affect the cost of the digital camera used in the image acquisition. In this paper, we present a comprehensive study of the effect of these two characteristics on the vehicle classification process in terms of accuracy and performance. We apply a set of different state-of-the-art image classifiers to the BIT-Vehicle and LabelMe data sets. Each data set is downscaled into different scales to generate a variety of spatial resolutions of each data set. Besides, we examine the effect of color by converting each color version to a gray-scale one. At last, we draw a valid conclusion in regards to the impact of these two characteristics (i.e., dimension and color) on the classification accuracy and performance of the image classification methods using more than 46 000 individual experiments. Experimental results show that there is no significant influence of both color and spatial resolutions of the vehicle images on the classification results obtained by most state-of-the-art image classification methods. However, there is a correlation between the spatial resolution and the processing time required by most image classification methods. Our findings can play an important role in saving not only money, but also time for vehicle-type classification systems.

A New Fast and Low-Cost Photogrammetry Method for the Engineering Characterization of Rock Slopes

Abstract: Digital photogrammetry (DP) represents one of the most used survey techniques in engineering geology. The availability of new high-resolution digital cameras and photogrammetry software has led to a step-change increase in the quality of engineering and structural geological data that can be collected. In particular, the introduction of the structure from motion methodology has led to a significant increase in the routine uses of photogrammetry in geological and engineering geological practice, making this method of survey easier and more attractive. Using structure from motion methods, the creation of photogrammetric 3D models is now easier and faster, however the use of ground control points to scale/geo-reference the models are still required. This often leads to the necessity of using total stations or Global Positioning System (GPS) for the acquisition of ground control points. Although the integrated use of digital photogrammetry and total station/GPS is now common practice, it is clear that this may not always be practical or economically convenient due to the increase in cost of the survey. To address these issues, this research proposes a new method of utilizing photogrammetry for the creation of georeferenced and scaled 3D models not requiring the use of total stations and GPS. The method is based on the use of an object of known geometry located on the outcrop during the survey. Targets located on such objects are used as ground control points and their coordinates are calculated using a simple geological compass and trigonometric formula or CAD 3D software. We present three different levels of survey using (i) a calibrated digital camera, (ii) a non-calibrated digital camera and (iii) two commercial smartphones. The data obtained using the proposed approach and the three levels of survey methods have been validated against a laser scanning (LS) point cloud. Through this validation we highlight the advantages and limitations of the proposed method, suggesting potential applications in engineering geology.

Off-the-Shelf Measurement Setup for Compressive Imaging

Abstract: While numerous works analyze the theoretical background of compressive sensing (CS) and provide rich mathematical theory, few practical implementations exist and they mostly share the same disadvantage of being either too complex or too expensive to implement. As a result of the reported work, a simple measurement setup for CS using off-the-shelf components is proposed. A simple and portable CS architecture consists of a digital camera, a projector, and mechanical integration elements. Extensive analysis of the proposed measurement system and the detailed calibration method is provided. The presented CS method allows for the use of a low-resolution camera with significant lens distortion for data acquisition. The results of our CS method are nondistorted, high-resolution images, which are obtained from the acquired measurement data using sparse optimization.

Automated Low-Cost Photogrammetric Acquisition of 3D Models from Small Form-Factor Artefacts

Abstract: The photogrammetric acquisition of 3D object models can be achieved by Structure from Motion (SfM) computation of photographs taken from multiple viewpoints. All-around 3D models of small artefacts with complex geometry can be difficult to acquire photogrammetrically and the precision of the acquired models can be diminished by the generic application of automated photogrammetric workflows. In this paper, we present two versions of a complete rotary photogrammetric system and an automated workflow for all-around, precise, reliable and low-cost acquisitions of large numbers of small artefacts, together with consideration of the visual quality of the model textures. The acquisition systems comprise a turntable and (i) a computer and digital camera or (ii) a smartphone designed to be ultra-low cost (less than $150). Experimental results are presented which demonstrate an acquisition precision of less than 40 μ m using a 12.2 Megapixel digital camera and less than 80 μ m using an 8 Megapixel smartphone. The novel contribution of this work centres on the design of an automated solution that achieves high-precision, photographically textured 3D acquisitions at a fraction of the cost of currently available systems. This could significantly benefit the digitisation efforts of collectors, curators and archaeologists as well as the wider population.

Depth from a polarisation + RGB stereo pair.

Abstract: In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shape-from-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to estimate diffuse albedo. By extending a previous shape-from-polarisation method to the perspective case, we show how to compute dense, detailed maps of absolute depth, while retaining a linear formulation. We show that our hybrid method is able to recover dense 3D geometry that is superior to state-of-the-art shape-from-polarisation or two view stereo alone.

Daylight-Artificial Light Integrated Scheme Based on Digital Camera and Wireless Networked Sensing-Actuation System

Abstract: Automated lighting can achieve significant energy savings. A daylight-artificial light integrated system with the camera as the sensor and wireless sensor actuator networked (WSAN) system is presented here. The workplane luminance, the window luminance, and the discomfort glare position of the user are extracted from the image of the workspace captured by the camera. The findings of this paper are the usage of the camera as luminance meter and how this information is used in the control of LED dimming based on consumer comfort. A novel camera-based fuzzy controller for window blind, considering visual, and thermal comfort, is designed, based on the parameters extracted from the image, to optimize the illuminance and uniformity for a test space. The control system integrates luminaire and window blind control. The model-based design approach provides visual and thermal comfort for the consumer without compromising on energy consumption. The real time implementation of the shading and lighting integrated model, with daylight adaptation and the wireless networked sensor-actuation system is shown in this paper. The performance of the wireless networked lighting scheme is analyzed, by evaluating the energy consumption of the nodes in idle, transmit, and receive mode.

Learning Digital Camera Pipeline for Extreme Low-Light Imaging

Abstract: In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR). We present a data-driven approach that learns the desired properties of well-exposed images and reflects them in images that are captured in extremely low ambient light environments, thereby significantly improving the visual quality of these low-light images. We propose a new loss function that exploits the characteristics of both pixel-wise and perceptual metrics, enabling our deep neural network to learn the camera processing pipeline to transform the short-exposure, low-light RAW sensor data to well-exposed sRGB images. The results show that our method outperforms the state-of-the-art according to psychophysical tests as well as pixel-wise standard metrics and recent learning-based perceptual image quality measures.

Crack Automatic Detection of CCTV Video of Sewer Inspection with Low Resolution

Abstract: In South Korea, sewage pipeline exploration devices have been developed using high resolution digital camera of 2 mega-pixels or above. However, most of the devices are less than 300 kilo-pixels due to poverty of the business. Moreover, since 100 kilo-pixels devices are widely used, environment for image processing is very poor. In this study, we adapted very low resolution (240 × 320 = 76,800 pixels) images by which it is difficult to detect cracks. Considering that images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique has been studied using digital image processing technology for low resolution images of sewage pipeline. Authors have developed a program to automatically detect cracks as per eight steps based on MATLAB’s functions. In this study, the third step covers an algorithm developed to find optimal threshold value, and the sixth step deals with algorithm to determine cracks. When we select [1.11:1.29] of zero padding range, exact detection rate is 89.2% and error rate is 4.44%. As the result, in spite of very low-resolution images, the performance of crack detection turned out to be excellent.

Investigations into the quality of image-based point clouds from uav imagery

Abstract: . The quality of image-based point clouds generated from images of UAV aerial flights is subject to various influencing factors. In addition to the performance of the sensor used (a digital camera), the image data format (e.g. TIF or JPG) is another important quality parameter. At the UAV test field at the former Zollern colliery (Dortmund, Germany), set up by Bochum University of Applied Sciences, a medium-format camera from Phase One (IXU 1000) was used to capture UAV image data in RAW format. This investigation aims at evaluating the influence of the image data format on point clouds generated by a Dense Image Matching process. Furthermore, the effects of different data filters, which are part of the evaluation programs, were considered. The processing was carried out with two software packages from Agisoft and Pix4D on the basis of both generated TIF or JPG data sets. The point clouds generated are the basis for the investigation presented in this contribution. Point cloud comparisons with reference data from terrestrial laser scanning were performed on selected test areas representing object-typical surfaces (with varying surface structures). In addition to these area-based comparisons, selected linear objects (profiles) were evaluated between the different data sets. Furthermore, height point deviations from the dense point clouds were determined using check points. Differences in the results generated through the two software packages used could be detected. The reasons for these differences are filtering settings used for the generation of dense point clouds. It can also be assumed that there are differences in the algorithms for point cloud generation which are implemented in the two software packages. The slightly compressed JPG image data used for the point cloud generation did not show any significant changes in the quality of the examined point clouds compared to the uncompressed TIF data sets.

Finding a Colour Filter to Make a Camera Colorimetric by Optimisation

Abstract: The Luther condition states that a camera is colorimetric if its spectral sensitivities are a linear transform from the XYZ colour matching functions. Recently, a method has been proposed for finding the optimal coloured filter that when placed in front of a camera, results in effective sensitivities that satisfy the Luther condition. The advantage of this method is that it finds the best filter for all possible physical capture conditions. The disadvantage is that the statistical information of typical scenes are not taken into account.

Assessing a 35mm Fixed-Lens Sony Alpha-5100 Intrinsic Parameters Prior to, During, and Post UAV Flight Mission

Abstract: A fixed focal length lens (FFL) camera with on-adjustable focal length is common companions for conducting aerial photography using unmanned aerial vehicles (UAVs) due to its superiority on optical quality and wider maximum aperture, lighter weight and smaller sizes. A wide-angle 35mm FFL Sony a5100 camera had been used extensively in our recent aerial photography campaign using UAV. Since this off-the-self digital camera is categorized into a non-metric one, a stability performance issue in terms of intrinsic parameters raises a considerably attention, particularly on variations of the lens principal distance and principal point's position relative to the camera's CCD/CMOS sensor caused by the engine and other vibrations during flight data acquisitions. A series of calibration bundle adjustment was conducted to determine variations in the principal distances and principal point coordinates before commencing, during, and after accomplishment of the flight missions. This paper demonstrates the computation of the parameters and presents the resulting parameters for three different epochs. It reveals that there are distinct discrepancies of the principal distances and principal point coordinates prior to, during, and after the mission, that peaked around 1.2mm for the principal distance, as well as around 0.4mm and 1.3mm along the x-axis and the y-axis of the principal point coordinates respectively. In contrast, the lens distortions parameters show practically no perturbations in terms of radial, decentering, and affinity distortion terms during the experiments.

Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring

Abstract: Abstract In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the second and third procedures in comparison to the first one. In addition, the results show that the second procedure is computationally more efficient than the other two.

Correct use of color for cultural heritage documentation

Abstract: . Accurate color recording is a fundamental feature for proper cultural heritage documentation, cataloging and preservation. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of digital enhancement techniques only. The objective of this study is to apply a rigorous procedure for the characterization of cameras based on a second-order polynomial model. Trichromatic digital cameras capture color information in the well-known RGB format. Nevertheless, the signal generated by the digital camera is device dependent. By means of the characterization, we establish the relationship between device-dependent RGB values and the tristimulus coordinates defined by the CIE standard colorimetric observer. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. We applied the methodology on a set of pictures targeting Levantine rock art motifs in Cova Civil (Castellon, Spain) which is considered part of a UNESCO World Heritage Site. We used raw image files, with different exposure conditions, with raw RGB values captured by the sensor. The outcomes obtained are satisfactory and very promising for proper color documentation in cultural heritage documentation.