Transmission Map Research Articles

Large Interferometer For Exoplanets (LIFE)

Context. In the fourth paper in this series, we identified that a pentagonal arrangement of five telescopes, using a kernel-nulling beam combiner, shows notable advantages for some important performance metrics for a space-based mid-infrared nulling interferometer over several other considered configurations for the detection of Earth-like exoplanets around solar-type stars. Aims. We aim to produce a physical implementation of a kernel-nulling beam combiner for such a configuration, as well as a discussion of systematic and stochastic errors associated with the instrument. Methods. We developed a mathematical framework around a nulling beam combiner, and then used it along with a space interferometry simulator to identify the effects of systematic uncertainties. Results. We find that errors in the beam combiner optics, systematic phase errors and the root-mean-squared (RMS) fringe tracking errors result in instrument-limited performance at ~4–7 μm, and zodiacal light limited at ≳10 μm. Assuming a beam splitter reflectance error of |ΔR| = 5% and phase shift error of Δϕ = 3°, we find that the fringe tracking RMS error should be kept to less than 3 nm in order to be photon limited, and the systematic piston error be less than 0.5 nm to be appropriately sensitive to planets with a contrast of 1 × 10−7 over a 4–19 μm bandpass. We also identify that the beam combiner design, with the inclusion of a well-positioned shutter, provides an ability to produce robust kernel observables even if one or two collecting telescopes were to fail. The resulting four-telescope combiner, when put into an X-array formation, results in a transmission map with a relative signal-to-noise ratio equivalent to 80% of a fully functioning X-array combiner. Conclusions. The advantage in sensitivity and planet yield of the Kernel-5 nulling architecture, along with an inbuilt contingency option for a failed collector telescope, leads us to recommend this architecture be adopted for further study for the LIFE mission.

Single image dehazing via color balancing and quad-decomposition atmospheric light estimation

In recent years, single image dehazing methods have achieved good performance. But there still exists some problems, such as color distortion, excessive smoothing of image texture details and image over-saturation. For solving these problems, this paper proposes an improved single image dehazing algorithm based on Color Balancing and Quad-Decomposition (CBQD). In the pre-processing stage, we define an indicator for detecting image unbalance color channel, and correct color of images that are prone to color distortion in some special environments (e.g., sandstorms). In the dehazing phase of the algorithm, we apply the improved quad-decomposition technique for eliminating the interference of non-atmospheric light source and certain bright objects, thus can estimate atmospheric light accurately. Meanwhile, we obtain the image depth information by the linear model and estimate the initial image transmission map by fused depth map, and then refine the transmission map using a gradient domain-guided filter with edge-preserving characteristics. Finally, we can restore the scene radiance, and the dehazed images have clear edges, rich texture, no halo artifacts, and natural color. Extensive experiments verifies the superiority of CBQD over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency.

Remote Sensing Image Dehazing Using Heterogeneous Atmospheric Light Prior

Remote sensing images (RSIs) captured in haze weather will suffer from serious quality degradation with color distortion and contrast reduction, which creates numerous challenges for the utilization of RSIs. To address these issues, this paper proposes a novel haze removal algorithm, named HALP, for visible RSIs based on a heterogeneous atmospheric light prior and side window filter. HALP is comprised of two key components. Firstly, given the large imaging space of RSIs, the atmospheric light is treated as a globally non-uniform distribution instead of a global constant. Therefore, a simple and effective method for non-uniform atmospheric light estimation is presented, which utilizes the brightest pixel color in each local image patch as the atmospheric light of the local region. Secondly, a side window filter-based transmission estimation algorithm is proposed, which can effectively suppress the block effect in the transmission map caused by the large window of the minimum filter used in the dark channel algorithm. Experiments on both real-world and synthetic remote sensing haze images demonstrate the effectiveness of HALP. In terms of no-reference and full-reference image quality assessments, HALP yields excellent results, outperforming existing state-of-the-art algorithms, including physics-based and neural network-based methods. The visual comparison of dehazed results also shows that HALP can restore degraded RSIs with uneven haze, producing clear images with rich details and natural colors.

U-shape Transformer for Underwater Image Enhancement.

The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we built a large scale underwater image (LSUI) dataset, which covers more abundant underwater scenes and better visual quality reference images than existing underwater datasets. The dataset contains 4279 real-world underwater image groups, in which each raw image's clear reference images, semantic segmentation map and medium transmission map are paired correspondingly. We also reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module specially designed for UIE task, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority. The dataset and demo code are available on https://bianlab.github.io/.

Deterministic Modeling for Radiation Attenuation-integrated Radon Transform in Emission Computed Tomography: Algorithm, Curve Fitting Analysis, and Introduction of Attenuation Hadamard Matrix.

The purpose of the study is to propose an algorithm to implement and visualize radiation attenuation-integrated Radon transform based on Beer-Lambert law during emission computed tomography simulation using a deterministic model and also to perform image analysis on resulting images. Two types of phantoms are designed: plain-disk phantom and patterned-disk phantom. The large disk is filled with an activity of 5 units and the smaller disks have 10 units of activity of 99mTc isotope as an emission map. Three transmission maps for patterned-disk phantom are created with uniform linear attenuation coefficient. Phantoms are scanned with 360° and 180° acquisition arcs. Then, using the algorithm designed, the exponential Radon transform is implemented. After that, the projections are back-projected and filtered to generate tomographic slices. Finally, all slices are analyzed using profile plotting and curve fitting. Moreover, an attenuation Hadamard matrix is provided to facilitate attenuation modeling. The uniform intensity of activity in the phantoms is converted to a disk with progressively decreasing intensity from the periphery to the center in the tomographic slices. Similarly, the circles positioned more centrally appear less intense than those positioned in the periphery, despite all circles having equal activity. When the phantom is scanned in 180° arc, the circles closest to the camera are visualized more intensely. The profile curves of the slices generated by exponential Radon transformation are depicted as U-shaped in profile plotting and are fitted to a bi-exponential function with a near-perfect precision. The incorporation of radiation attenuation results in the development of more realistic models for quantification purposes.

Atmospheric Scattering Model Induced Statistical Characteristics Estimation for Underwater Image Restoration

Underwater images often suffer from color deviation and low contrast due to selective absorption and light scattering, whose degradation is generally described by an Atmospheric Scattering Model (ASM). However, it is challenging to design hand-craft priors to estimate the transmission map and global light within ASM. To avoid the estimation on these two variables, in this paper, we establish a statistical characteristics relationship between underwater and recovered images based on ASM. With this relationship, a novel lightweight model is proposed for efficient Underwater Image Restoration (UIR). Within our proposed model, the UIR problem is disentangled into global restoration and local compensation, for which two modules are developed. Extensive experimental results demonstrate that our proposed method can effectively improve color deviation and low contrast while preserving details, and outperform state-of-the-art methods.

Aerial image defogging method based on nonlocal feature structure tensor by UAV cameras with three-channel RGB cameras

The application of unmanned aerial system (UAV) remote sensing technology as a unique means of spatial data collection in various fields is based on high-resolution images captured by UAVs. However, due to the influence of atmospheric water vapor, haze, and other factors, the quality of UAV aerial images is limited. Considering that the model-based image fogging method developed according to the dark channel theory is widely used, the image edge after fogging incurs a “halo” effect owing to the excessive coarseness of the transmission map. We take advantage of the nonlocal structure tensor of the image to better protect the important details of the image edge while removing noise. The images we used are obtained from the public multi real world fog image dataset. Experiment results demonstrate that the proposed method can effectively eliminate the halo effect that occurs at the edges of an image after demisting.

1902. Whole Genome Sequence analysis of early community transmission SARS-CoV-2 samples in the Philippines

Abstract Background The Philippines has recorded nearly 3.7 million COVID-19 cases and more than 60,000 deaths. The first three cases of COVID-19 in the country were reported in January 2020 (1 lineage A, 2 lineage B, all Chinese tourists), with the first death outside China occurring among these cases. No cases were reported in February 2020. The first Filipino cases were reported March 2020. With over one million overseas Filipinos repatriated, tracking the introduction of waves of variants and subsequent community spread is important in order to improve future control efforts. Methods We analyzed 35 archived samples collected from March to September 2020 at the Philippines National Institutes of Health. SARS-CoV2 RT-PCR positive samples with Ct values from 15 to 25 were sequenced using Illumina HiSeq and analyzed using an in-house bioinformatics pipeline. Lineages were assigned using PANGOLIN. 14 whole genome sequences were generated with sufficient coverage. These were analyzed with 439 whole genome sequences from February to September 2020 that were publicly available on ViruSurf and GISAID to generate transmission maps. Results Transmission maps are shown in Figure 1. The earliest community transmission of COVID-19 in the Philippines was from lineage B.6 in March 2020 most closely related to B.6 from India. There is no evidence of onward transmission from the initial reported cases in January 2020 (lineage A and B). In June 2020, the predominant lineage switched to B.1 and its sublineages from Europe, coincident with a surge in cases. Figure 1.Alignment of the whole genome sequences from early Philippine samples and related SARS-COV2 sequences from ViruSurf and GISAID. Conclusion Transmission maps are shown in Figure 1. The earliest community transmission of COVID-19 in the Philippines was from lineage B.6 in March 2020 most closely related to B.6 from India. There is no evidence of onward transmission from the initial reported cases in January 2020 (lineage A and B). In June 2020, the predominant lineage switched to B.1 and its sublineages from Europe, coincident with a surge in cases. Disclosures Edsel Maurice Salvana, MD, MSD: Advisor/Consultant|MSD: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Honoraria.

Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Underwater light absorption and scattering lead to color deviation, low brightness, fuzzy details and low contrast of underwater images. In this contribution, an underwater image enhancement algorithm based on color balance and multi-scale fusion is proposed. Firstly, a color balance method is used to correct the image color. Then, based on an improved dark channel prior algorithm, the local contrast information of the color balanced image is used to derive two atmospheric lights and transmission maps, which are transformed into the images with enhanced contrast and brightness, respectively. Finally, the multi-scale fusion method is adopted to fuse the contrast enhanced image and the brightness improved image according to the weights. The proposed algorithm is compared with other underwater image enhancement algorithms in qualitative and quantitative evaluation. Experimental results show that the proposed algorithm can effectively eliminate color deviation, remove dark areas, and improve brightness and contrast of underwater images. The enhanced images generated by the proposed algorithm are superior to those generated by other algorithms in indicators of PSNR, SSIM, UIQM and UCIQE. Thus, the proposed algorithm is suitable for underwater image enhancement.

Sand-Dust Image Enhancement Using Chromatic Variance Consistency and Gamma Correction-Based Dehazing.

In sand-dust environments, the low quality of images captured outdoors adversely affects many remote-based image processing and computer vision systems, because of severe color casts, low contrast, and poor visibility of sand-dust images. In such cases, conventional color correction methods do not guarantee appropriate performance in outdoor computer vision applications. In this paper, we present a novel color correction and dehazing algorithm for sand-dust image enhancement. First, we propose an effective color correction method that preserves the consistency of the chromatic variances and maintains the coincidence of the chromatic means. Next, a transmission map for image dehazing is estimated using the gamma correction for the enhancement of color-corrected sand-dust images. Finally, a cross-correlation-based chromatic histogram shift algorithm is proposed to reduce the reddish artifacts in the enhanced images. We performed extensive experiments for various sand-dust images and compared the performance of the proposed method to that of several existing state-of-the-art enhancement methods. The simulation results indicated that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.

Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model

Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method.

Image dehazing with hybrid total variation–L0 regularization

We propose a dehazing problem model with hybrid regularization and design an effective algorithm to restore the latent image and transmission map simultaneously. In the proposed dehazing problem model, we use the total variation (TV) to regularize the latent image and adopt the hybrid TV and L0-norm (TV–L0) regularization to model the transmission map. In the proposed optimization algorithm, we first use the dark channel prior to achieve an initial guess of the global atmospheric light and transmission map. Then we convert the original problem into two subproblems: one aims to update the latent image based on TV regularization, whereas the other estimates the transmission map with hybrid TV–L0 regularization. Both of the subproblems can be solved efficiently with variable splitting and penalty technology, and the minimizer is reached by alternately solving the two subproblems. Experimental results show that our approach can achieve a high-quality restored image that is comparable to some state-of-the-art methods.

Sandstorm Image Enhancement Using Image-Adaptive Eigenvalue and Brightness-Adaptive Dark Channel Network

Sandstorm images suffer from dust particles and the attenuation of light. Degraded sandstorm images have a reddish or yellowish color cast owing to dust particles, which have a certain color. Because of this phenomenon, distorted sandstorm images have imbalanced red and blue channels. Existing sandstorm enhancement methods improve the image by only focusing on dehazing, due to sandstorm images having similar features to hazy or dusty images. However, the enhanced images using previous methods have a color cast. Therefore, to enhance the sandstorm image naturally, a balancing procedure of color components is needed. This paper proposes a color-balancing method using image-adaptive eigenvalues. Because eigenvalues describe image characteristics, improved images have balanced color components. However, these enhanced images have dusty features. Therefore, to enhance balanced images a dehazing procedure is needed, something which this paper applies using a multiscale convolution neural network, thus generating a transmission map with brightness-adaptive features. Images enhanced using the proposed method compare well with state-of-the-art methods in subjective and objective measures of quality.

Unsupervised Enhancement and Web Tool for Perceptually Invisible Type Degraded Image

Severe contrast degradation in an image result in a substantial loss of crucial visible information. A novel unsupervised image enhancement algorithm is proposed in this paper to unravel hidden visual details in perceptually invisible images. In this paper, new algorithms are designed to solve the range localization problem. The proposed algorithm intends to expand the image range to maximize its intensity level utilization. Outliers present in an image intensity range are truncated based on their power distribution. Based on the type of input image, maxima-minima, or frequency-based expansion of truncated levels, is exercised to avoid over-enhancement. Two novel parameters, namely the transmission map and zero-frequency spread, are coined. The visually enhanced result successfully exhibits the camouflaged information and avoids over-enhancement. Consumer gadgets, such as cameras, have difficulty taking pictures in very dark and cloudy settings. The suggested algorithm can improve low-contrast photos that are both very dark and severely attenuated. The unsupervised nature of the proposed algorithm makes it suitable for application in the field of consumer electronics like mobile phones and digital cameras. A consumer-focused, platform-independent app is created and made available to the general public for additional study.

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model.

We propose a polarized image defogging algorithm according to the sky segmentation results and transmission map optimization. Firstly, we propose a joint sky segmentation method based on scene polarization information, gradient information and light intensity information. This method can effectively segment the sky region and accurately estimate the global parameters such as atmospheric polarization degree and atmospheric light intensity at infinite distance. Then, the Gaussian filter is used to solve the light intensity map of the target, and the information of the polarization degree of the target is solved. Finally, based on the segmented sky region, a three-step transmission optimization method is proposed, which can effectively suppress the halo effect in the reconstructed image of large area sky region. Experimental results shows that defogging has a big improvement in the average gradient of the image and the grayscale standard deviation. Therefore, the proposed algorithm provides strong defogging and can improve the optical imaging quality in foggy scenes by restoring fog-free images.

Underwater image restoration through regularization of coherent structures

Restoration of underwater images plays a vital role in underwater target detection and recognition, underwater robots, underwater rescue, sea organism monitoring, marine geological survey, and real-time navigation. Mostly, physics-based optimization methods do not incorporate structural differences between the guidance and transmission maps (TMs) which affect the performance. In this paper, we propose a method for underwater image restoration by utilizing a robust regularization of coherent structures. The proposed method incorporates the potential structural differences between TM and the guidance map. The optimization of TM is modeled through a nonconvex energy function which consists of data and smoothness terms. The initial TM is taken as a data term whereas the smoothness term contains static and dynamic structural priors. Finally, the optimization problem is solved using majorize-minimize (MM) algorithm. The proposed method is tested on benchmark dataset and its performance is compared with the state-of-the-art methods. The results from the experiments indicate that the proposed regularization scheme adequately improves the TM, which results in high-quality restored images.

Single underwater image haze removal with a learning-based approach to blurriness estimation

Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.

Tracking the Outbreak of Carbapenem-Resistant Klebsiella pneumoniae in an Emergency Intensive Care Unit by Whole Genome Sequencing.

The spread of carbapenem-resistant Klebsiella pneumoniae (CRKP) has become a great threat to human health, especially in the intensive care unit. The aim of this study was to identify the origin and transmission route of a CRKP outbreak in an emergency intensive care unit (EICU), so as to provide prevention and control strategies for CRKP outbreak. Between Mar and Jun 2018, 10 CRKP isolates from 5 patients in the EICU ward of Shanghai Ruijin hospital north were collected. Modified carbapenem inactivation method (mCIM) and whole-genome sequencing (WGS) were performed on all 10 CRKP isolates. By integrating the genomic and epidemiological data of our isolates and 9 CRKP isolates from an outbreak in another hospital, a putative transmission map was constructed. All 10 outbreak strains were carbapenemase positive in mCIM and belonged to the sequence type 11 (ST11) clone, harbored a set of resistance genes and virulence genes. The phylogenetic tree of CRKP isolates based on two outbreaks revealed that the initial isolate A1 in our EICU ward belonged to one branch of isolates in another hospital, this introductive isolate evolved and caused a subsequent outbreak in our EICU. Integration of genomic and epidemiological data can yield a clear transmission map of CRKP outbreak. Monitoring the rapid evolution of CRKP at the early stage of outbreak, CRKP monitoring after patients are discharged, active surveillance of newly admitted patients, environmental hygiene and efficient antibiotic treatment may be the key to prevent and control of CRKP outbreak.

Robust U‐Net: Development of robust image enhancement model using modified U‐Net architecture

SummaryThe image dehazing stage is used significantly as a preprocessing step for various applications such as remote sensing and long range imaging and automatic driver assistance system. Images acquired under low illumination, fog and snow conditions frequently show qualities like low contrast and low brightness, which genuinely influence the enhanced abstract visualization on natural eyes and extraordinarily limit the exhibition of different machine vision frameworks. The images that are captured in low‐light or heavy fog might have salient features that cannot be extracted using standard computer vision systems. A good way to get the enhanced image is to determine the transmission map (haze density or low illumination parameters) of air‐light media from each pixel of the input image. In this article, an improved U‐Net architecture is proposed to enhance images and provide robust performance metrics against the existing methods. In this model, the pooling operations in generalized U‐Net architecture are replaced by discrete wavelet transform based on up and down samplings. An attention module is developed by fusing both up and down samples to identify the missing information of low‐level features in up‐samples. The proposed architecture for U‐Net tested with different datasets: See‐in‐the‐Dark (SID) dataset, Exclusively Dark Image Dataset (ExDark), Realistic Single Image Dehazing (RESIDE) dataset, and few real‐time images and achieves superior performance metrics in terms of PSNR, MSE, and SSI when compared to the other state‐of‐art methods.

Cloud Detection of Gaofen-2 Multi-Spectral Imagery Based on the Modified Radiation Transmittance Map

For optical remote sensing images with high spatial resolution and low spectral number, the complexity of ground objects poses great challenges to cloud detection algorithms, such as the differentiation of clouds from objects with similar features as clouds and the identification of thin clouds. In this paper, a novel cloud detection method is proposed for Gaofen-2 remote sensing imagery. The radiation transmittance is estimated based on the dark channel prior, and the overestimated radiation transmittance is corrected using spectral features. A three-step post-processing strategy is adopted to eliminate misidentification introduced by the highlighted surfaces based on object geometric, textural, and boundary features. In the experiments, Gaofen-2 multispectral images with different cloud categories and cloud thicknesses are involved to evaluate the performance of the proposed method. The results show that the proposed method can obtain an average cloud detection accuracy of 0.9573 on six different clouds. The proposed algorithm can also effectively detect both thick and thin clouds with an average accuracy of more than 0.9517. The advantages of the method for thin cloud detection are further demonstrated by comparison with existing algorithms.