Background Suppression Method Research Articles

Optical-domain modulation cancellation method for background-suppression and dual-gas detection in light-induced thermo-elastic spectroscopy

To address the issues of signal saturation and measurement range reduction caused by the background in light-induced thermoelastic spectroscopy (LITES) employing the first harmonic (1 f) modulation, an optical-domain modulation cancellation method (MOCAM) was reported in the 1f-based LITES. A balance laser source with the opposite sine modulation phase as the excitation laser source synchronously excites the quartz tuning fork (QTF) to realize background suppression. By optimizing the modulation amplitude of the balance laser and the modulation phase difference between the two laser sources, the background level was suppressed by two orders of magnitude from ∼ 7 mV to ∼ 18 μV, close to the QTF thermal noise level (∼ 1 μV). Combining the excitation laser with the balance laser enables MOCAM to overcome common mode interferences, e.g. optical path shift, laser power fluctuation, and focus position change. Furthermore, methane and water vapor (CH4/H2O) dual-gas detection based on MOCAM and time-division multiplexing method was demonstrated, and measurement range is enlarged with easy calibration due to zero background. The MOCAM-based 1f-LITES technique delivers effective background suppression and provides robustness to common mode interferences, thus establishes a feasible solution for mobile vehicle or airborne gas monitoring applications with serious environmental disturbances.

Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics

Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored.

Joint spatio-temporal features and sea background prior for infrared dim and small target detection

The detection of dim and small targets in the complex sea background poses new challenges to automatic target search and interception by infrared air-to-air missiles at sea. To solve the problems of low detection rate of dim targets and massive suspected targets caused by the shadow region of clutter on the sea surface, this paper proposes a detection method for dim and small targets with spatio-temporal features and background prior information. Firstly, by combining the ideas of local energy factor and multi-scale patch contrast measure, an improved dim target detection method is proposed, which can effectively enhance the contrast of dim targets and suppress background clutter. Then, according to the grayscale probability distribution difference between the background and the target, a double-Gaussian model is established for the residual background and the target region and a background suppression method based on the spatial prior information is proposed to further suppress the residual background. Finally, a dim target detection method is proposed by combining the above dim target detection, background suppression, and dynamic pipeline filtering methods. The results indicate the average detection rate of the detection method proposed in this study is 91.44%. Also, the average number of false alarms in a single frame is reduced to 9.89, which is more than 80% lower than that of other algorithms.

Study of background from accidental coincidence signalsin the PandaX-II experiment* *Supported in part by a grant from the Ministry of Science and Technology of China (2016YFA0400301), National Science Foundation of China (12090060, 12005131, 11905128, 11925502, 11775141), and Office of Science and Technology, Shanghai Municipal Government (18JC1410200)

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved.

Novel 2D velocity estimation method for large transient events in plasmas

Dynamics of fast transient events are challenging to be analyzed with high time resolution. Such events can occur in fusion plasmas such as the filaments during edge-localized modes (ELMs). In this paper, we present a robust method-the spatial displacement estimation-for estimating the displacements of structures with fast dynamics from high spatial and time resolution imaging diagnostics [e.g., gas-puff imaging (GPI)] with sampling time temporal resolution. First, a background suppression method is shown, which suppresses the slowly time-evolving and spatially non-uniform background in the signal. In the second step, a two-dimensional polynomial trend subtraction method is presented to tackle the remaining polynomial order trend in the signal. After performing these pre-processing steps, the spatial displacement of the propagating structure is estimated from the two-dimensional spatial cross-correlation coefficient function calculated between consecutive frames. The method is tested for its robustness and accuracy by simulated Gaussian events and spatially displaced random noise. An example application of the method is presented on propagating ELM filaments measured by the GPI system on the National Spherical Torus Experiment spherical tokamak.

Time-resolved polarization lock-in filtering for background suppression in Raman spectroscopy of biomass pyrolysis

Laser-based Raman spectroscopy is a powerful technique for non-intrusive measurements of chemical composition in gas, liquid, and solids. However, weak signals make it challenging to employ the technique for diagnostics under harsh conditions with various background interferences. To overcome such limitations, we have devised a method, polarization lock-in filtering (PLF) based on temporal modulation of the excitation laser polarization, to filter out polarization-independent signals from acquired data. The PLF method applied for continuous Raman spectroscopy measurements of a biomass pyrolysis process showed promising filtering abilities for unwanted background fluorescence signals. A broadband fluorescence background interference was suppressed by up to a factor of 50. Therefore, released species during the biomass pyrolysis process were readily identified with their Raman spectrum signatures and their amounts quantified. In addition, the PLF method provided Raman spectra of low background, from which a gradual change in hydrocarbons released at different stages during the pyrolysis could be observed. Altogether, the efficient background suppression method increases the general applicability of Raman spectroscopy under conditions where interfering signals present a challenge and a limiting factor.

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection.

Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.

Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

To improve the detection of dim and small targets in infrared (IR) images containing high-intensity cloud clutter, a novel adaptive background suppression method is proposed. By using three-dimensional cooperative filtering and differential calculation, the different and complex background clutter is suppressed. To obtain the optimal parameters for the background suppression algorithm, an adaptive parameter optimization method is proposed. The adaptive parameter optimization problem is transformed into a multiobjective optimization problem in which the signal-to-clutter ratio gain and background suppression factor, which effectively reflect the background suppression performance, are chosen as the optimization objectives, and the parameters of the proposed background suppression algorithm are considered as the variables. To effectively solve the established multiobjective optimization problem, a particle swarm parameter optimization-based method is utilized. Experimental results indicate that the proposed adaptive background suppression method using these optimal parameters has good performance for IR images in real complex scenes, as well as performance superior to that of other baseline methods.

Effective online refinement for video object segmentation

In this paper, we propose a novel framework, which deeply explores the motion cue and the online fine-tuning strategy to tackle the task of semi-supervised video object segmentation. First, in order to filter out the irrelevant background regions in the initial segmentation results, which are generated by an existing semi-supervised segmentation model, a motion based background suppression method is exploited to obtain the purified segmentation results. Second, a set of key frames with high-quality segmentation results are selected based on several metrics of segmentation quality in the purified segmentation results. Finally, the selected key frames are combined with the manually annotated first frame to efficiently retrain the segmentation model online, so as to obtain more accurate segmentation results. Our experimental results on two challenging datasets demonstrate that the proposed framework achieves the state-of-the-art performance.

Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement

Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algorithm for the target enhancement of xylene spills and their segmentation in ultraviolet (UV) images. To improve the contrast between targets and backgrounds (waves, sun reflections, and shadows), we developed a global background suppression (GBS) method to remove the irrelevant objects from the background, which is followed by an adaptive target enhancement (ATE) method to enhance the target. Based on the histogram information of the processed image, we designed an automatic algorithm to calculate the optimal number of clusters, which is usually manually determined in traditional cluster segmentation methods. In addition, necessary pre-segmentation processing and post-segmentation processing were adopted in order to improve the performance. Experimental results on our UV image datasets demonstrated that the proposed method can achieve good segmentation results for chemical spills from different backgrounds, especially for images with strong waves, uneven intensities, and low contrast.

Weighted Background Suppression Target Detection Using Sparse Image Enhancement Technique for Newly Grown Tree Leaves

The process from leaf sprouting to senescence is a phenological response, which is caused by the effect of temperature and moisture on the physiological response during the life cycle of trees. Therefore, detecting newly grown leaves could be useful for studying tree growth or even climate change. This study applied several target detection techniques to observe the growth of leaves in unmanned aerial vehicle (UAV) multispectral images. The weighted background suppression (WBS) method was proposed in this paper to reduce the interference of the target of interest through a weighted correlation/covariance matrix. This novel technique could strengthen targets and suppress the background. This study also developed the sparse enhancement (SE) method for newly grown leaves (NGL), as sparsity has features similar to newly grown leaves. The experimental results suggested that using SE-WBS based algorithms could improve the detection performance of NGL for most detectors. For the global target detection methods, the SE-WBS version of adaptive coherence estimator (SE-WBS-ACE) refines the area under the receiver operating characteristic curve (AUC) from 0.9417 to 0.9658 and kappa from 0.3389 to 0.4484. The SE-WBS version of target constrained interference minimized filter (SE-WBS-TCIMF) increased AUC from 0.9573 to 0.9708 and kappa from 0.3472 to 0.4417; the SE-WBS version of constrained energy minimization (SE-WBS-CEM) boosted AUC from 0.9606 to 0.9713 and kappa from 0.3604 to 0.4483. For local target detection methods, the SE-WBS version of adaptive sliding window CEM (ASW SE-WBS-CEM) enhanced AUC from 0.9704 to 0.9796 and kappa from 0.4526 to 0.5121, which outperforms other methods.

A Fast Fluorescence Background Suppression Method for Raman Spectroscopy Based on Stepwise Spectral Reconstruction

Raman spectroscopy is a rapid and non-destructive technique for detecting unique spectral fingerprints from biological samples. Raw Raman spectra often come with strong fluorescence background that makes spectral interpretation challenging. Although fluorescence background can be suppressed experimentally, this approach requires sophisticated and costly instruments. For convenience and cost-effectiveness, numerical methods have been used frequently to remove fluorescence background. Unfortunately, many of such methods suffer from long computation time. Therefore, a fast numerical method for fluorescence suppression is highly desirable especially in Raman spectroscopic imaging where Raman measurements from many pixels need to be processed rapidly. In response to this demand, we propose a fast numerical method for fluorescence background suppression based on the strategy of stepwise spectral reconstruction that we previously developed. Compared with traditional computational methods, including polynomial fitting, wavelet transform, Fourier transform, and peak detection, our results consistently show significant advantages in both accuracy and computational efficiency when tested on Raman spectra measured from phantoms and cells as well as surfaced enhanced Raman spectra from blood serum samples. In particular, our method yields clean Raman spectra closest to the reference results generated by polynomial fitting while several orders of magnitude faster than others. Therefore, the proposed fast fluorescence suppression method is promising in Raman spectroscopic imaging or related application in which high-computation efficiency is critical and a calibration dataset is available.

Vehicle-logo recognition based on modified HU invariant moments and SVM

As a part of the vehicle identification system, the logo recognition, while matching with the license plate recognition, can be used to define the identity of the vehicle more accurately and provide reliable evidence for the deck car investigation, illegal escape and vehicle tracking. However, it is a difficult problem for the research to position the logos of different vehicles and the identification of the vehicles under low illumination conditions. This paper firstly uses the features of the color of the license plate to locate the license plate, and carries out the rough location of the logo according to the prior knowledge. Then, uses gray level, contrast enhancement, smoothing de-noising, edge detection and background suppression methods to deal with the coarse location of logo and realize the positioning of logo accurately. Next, extracts features of Vehicle-logo according seven HU invariant, considering the influence of low illumination conditions, this paper adds three HU invariant distances and establishes the characteristic library of the logo image. Thirdly, uses the support vector machine(SVM) to identify the logo and Cross validation(CV) methods to optimize the parameter C and g of SVM at the same time. In order to improve the recognition accuracy of the algorithm under low illumination conditions, the Grey Wolf Optimize (GWO) is used to further optimize the kernel function. Finally, takes 9 kinds of common Vehicle-logo as the logo to be identified, uses SVM to train 80% of the samples and test 20% of the samples. The results of experiments show that the increase of the invariant moments feature can obviously improve the accuracy of the logo, GWO is better than CV to improve the accuracy, and the average recognition rate is more than 92%, which effectively solve the problem of Vehicle-logo identification under low illumination conditions.

Infrared dim and small target background suppression based on improved gradient inverse weighting filter

Dim and small infrared target easily flooded in complicated background. In order to improve the ability of target detection, the background is often suppressed to enhance the target signal. Referring to the lack of robust adaptability of the gradient inverse weighted filtering for background edges , an improved gradient inverse weighting filtering algorithm is proposed through the establishment of background local correlation function. The use of background local statistical characteristics of adaptive filter parameters, can better adapt to the drastic change in the background, and improve the ability to suppress background suppression algorithm. Experimental results show that the improved gradient inverse weighted filtering could effectively suppress the background of images, presenting a superior overall performance to other background suppression methods.

Background Suppression for Infrared Dim and Small Target Detection Using Local Gradient Weighted Filtering

In this paper, a temporal local gradient weighted filtering based infrared dim small target background suppression method has been proposed. The highlight of the proposed method is that it utilizes the local gradient weighted filtering to deal with the temporal profile, which separates the small target from strong background clutter according to the difference of profile among small target, stable background and the edge of cloud. In this paper, two real low signal-to-clutter ratio (SCR) infrared imagery sequences containing moving dim small target are applied to verify the efficiency of the proposed method. Compared with several classical methods, the experimental results show that the performance of proposed method is superior to several other methods both in aspects of subjective visual and objective evaluation index.

Search for direct pair production of supersymmetric top quarks decaying to all-hadronic final states in pp collisions at [Formula: see text

Results are reported from a search for the pair production of top squarks, the supersymmetric partners of top quarks, in final states with jets and missing transverse momentum. The data sample used in this search was collected by the CMS detector and corresponds to an integrated luminosity of 18.9,text {fb}^text {-1} of proton-proton collisions at a centre-of-mass energy of 8;text {TeV} produced by the LHC. The search features novel background suppression and prediction methods, including a dedicated top quark pair reconstruction algorithm. The data are found to be in agreement with the predicted backgrounds. Exclusion limits are set in simplified supersymmetry models with the top squark decaying to jets and an undetected neutralino, either through a top quark or through a bottom quark and chargino. Models with the top squark decaying via a top quark are excluded for top squark masses up to 755;text {GeV} in the case of neutralino masses below 200;text {GeV}. For decays via a chargino, top squark masses up to 620;text {GeV} are excluded, depending on the masses of the chargino and neutralino.

Research of background suppression method based on spatial-temporal bilateral filter

Research of background suppression method based on spatial-temporal bilateral filter

Determination of absolute internal conversion coefficients using the SAGE spectrometer

A non-reference based method to determine internal conversion coefficients using the SAGE spectrometer is carried out for transitions in the nuclei of 154Sm, 152Sm and 166Yb. The Normalised-Peak-to-Gamma method is in general an efficient tool to extract internal conversion coefficients. However, in many cases the required well-known reference transitions are not available. The data analysis steps required to determine absolute internal conversion coefficients with the SAGE spectrometer are presented. In addition, several background suppression methods are introduced and an example of how ancillary detectors can be used to select specific reaction products is given. The results obtained for ground-state band E2 transitions show that the absolute internal conversion coefficients can be extracted using the methods described with a reasonable accuracy. In some cases of less intense transitions only an upper limit for the internal conversion coefficient could be given.

Adaptive method for the detection of infrared small target

Background suppression is an important problem in infrared small target detection. The two-dimensional least mean square (TDLMS) filter is a widely used method, but its performance will decline when targets are embedded in a complex cluttered background. To fill the gap, variable step-size TDLMS, the neighborhood analysis technique, and the edge-directional TDLMS filter are developed but still cannot achieve a satisfying performance. Here, an adaptive method for background suppression is proposed. According to different characteristics of the pixels in homogeneous/target regions and inhomogeneous regions, two basic filters are first designed. Then a fuzzy edge estimation factor is introduced to combine them into a uniform framework, in which the two basic filters can be switched automatically to fit different kinds of pixels. Finally, a new mechanism to update and propagate the coefficients of the prediction window is constructed. It makes sure that the adaptive method works smoothly and reveals a potential to be implemented in parallel. The experimental results demonstrate that the proposed method achieves significant improvement in background suppression and detection performance.

Multiscale random projection based background suppression of infrared small target image

The heavy clutters background in a single infrared image containing small targets is difficult to be efficiently suppressed using the traditional methods. To overcome this difficulty, a novel infrared image background suppression method based on multiscale random projection is proposed in this paper. On the one hand, using nonsubsampled pyramid transform that decomposes the original infrared image into a low-frequency subband and a series of high-frequency subbands, the proposed method separates the background and targets from the original infrared image. On the other hand, due to a subband image cube is formed by concatenating together all the high-frequency subbands and a random projection is applied to the image cube, the proposed method can well preserve target information and reduces the spatial redundancy of the target and background information. Experimental results on three real infrared image sequences under different clutters background demonstrate that the proposed method is efficient and can obtain better performance than other methods for background suppression of infrared small target images.