Multi-scale Background Research Articles

An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiency Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth’s climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment.

Dual channel and multi-scale adaptive morphological methods for infrared small targets

Infrared small target detection is a challenging task. Morphological operators with a single structural element size are easily affected by complex background noise, and the detection performance is easily affected by multi-scale background noise environments. In order to enhance the detection performance of infrared small targets, we propose a dual channel and multi-scale adaptive morphological method (DMAM), which consists of three stages. Stages 1 and 2 are mainly used to suppress background noise, while stage 3 is mainly used to enhance the small target area. The multi-scale adaptive morphological operator is used to enhance the algorithm’s adaptability to complex background environments, and in order to further eliminate background noise, we have set up a dual channel module. The experimental results indicate that this method has shown superiority in both quantitative and qualitative aspects in comparison methods, and the effectiveness of each stage and module has been demonstrated in ablation experiments. The code and data of the paper are placed in https://pan.baidu.com/s/19psdwJoh-0MpPD41g6N_rw.

An Improved Coupled Data Assimilation System with a CGCM Using Multi-Time-Scale High-Efficiency EnOI-Like Filtering

Abstract Coupled data assimilation (CDA), which combines coupled models and observations from multiple Earth system domains, plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. Traditional ensemble Kalman filter (EnKF) CDA algorithms, while convenient to implement in multiple DA components in a coupled system, are, however, expensive and lack sufficient representativeness for low-frequency background flows. Here, a multi-time-scale high-efficiency approximate filter with ensemble optimal interpolation (MSHea-EnOI) scheme has been implemented with a global fully coupled model. It consists of stationary, low-frequency, and high-frequency filters constructed from the time series of a single-model solution with improved representativeness for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnOI is evaluated in a biased twin experiment framework with synthetic “observations” produced by another coupled model, and a three-decade coupled reanalysis experiment with real observations. Results show that with increased representativeness on multiscale background flows, while computationally costing only a small fraction of ensemble-based CDA, the MSHea-EnOI shows the potential to improve CDA quality with synthetic observations. The coupled reanalysis experiment with real observations also shows reasonable fittings to observations and comparable results to other reanalysis products using different DA schemes. While reconstructing a close-to-rapid Atlantic meridional overturning circulation, the coupled reanalysis reproduces most of the atmosphere and ocean reanalysis signals such as the Hadley circulation and upper ocean heat content. The MSHea-EnOI could have good application potential in ensemble-based DA systems in terms of its multiscale property and computational efficiency.

MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images

Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors in high-resolution aerial images (HRAIs), such as imaging blur, background clutter, object shadow, and high resolution, substantially reduce the integrity and accuracy of object segmentation. By applying multisource data fusion, as in scene parsing of natural scene images, we can solve the aforementioned problems through the integration of auxiliary data into HRAIs. To this end, we propose a multiscale cross-layer interactive and similarity refinement network (MISNet) for scene parsing of HRAIs. First, in a feature fusion optimization module, we extract, filter, and optimize multisource features and further guide and optimize the features using a feature guidance module. Second, a multiscale context aggregation module increases the receptive field, captures semantic information, and extracts rich multiscale background features. Third, a dense decoding module fuses the global guidance information and high-level fused features. We also propose a joint learning method based on feature similarity and a joint learning module to obtain deep multilevel information, enhance feature generation, and fuse multiscale and global features to enhance network representation for accurate scene parsing of HRAIs. Comprehensive experiments on two benchmark HRAIs datasets indicate that our proposed MISNet is qualitatively and quantitatively superior to similar state-of-the-art models.

Shear Instability in Internal Solitary Waves in the Northern South China Sea Induced by Multiscale Background Processes

Abstract Instability within internal solitary waves (ISWs), featured by temperature inversions with vertical lengths of dozens of meters and current reversals in the upper shoreward velocity layer, was observed in the northern South China Sea at a water depth of 982 m by using mooring measurements between June 2017 and May 2018. Regions of shear instability satisfying Ri < 1/4 were found within those unstable ISWs, and some large ISWs were even possibly in the breaking state, indicated by the ratio of Lx (wave width satisfying Ri < 1/4) to λη/2 (wavelength at half amplitude) larger than 0.86. Wave stability analyses revealed that the observed wave shear instability was induced by strong background current shear associated with multiscale dynamic processes, which greatly strengthened wave shear by introducing sharp perturbations to the fine-scale vertical structures of ISWs. During the observational period, wave shear instability was strong in summer (July–September) while weak in winter (January–March). Sensitivity experiments revealed that the observed shear instability was prone to be triggered within large ISWs by the background current shear and sensitive to the pycnocline depth in the background stratification. However, shear instability within ISWs was observed to be promoted during mid-January, as the near-inertial waves trapped inside an anticyclonic eddy resulted in enhanced background current shear between 150 and 300 m. This work emphasizes the notable impacts of multiscale background processes on ISWs in the oceans.

Deep Image Inpainting With Enhanced Normalization and Contextual Attention

Deep learning-based image inpainting has been widely studied, leading to great success. However, many methods adopt convolution and normalization operations, which will bring up some issues to affect the performance. The vanilla normalization cannot distinguish the pixels in corrupted regions from the other valid pixels, resulting in the mean and variance shifts. In addition, the limited receptive field of convolution makes it unable to capture long-range valid information directly. In order to tackle these challenges, we propose a novel deep generative model for image inpainting with two key modules, namely, the channel and spatially adaptive batch normalization (CSA-BN) module, and the selective latent-space-mapping-based contextual attention (SLSM-CA) layer. We replace the vanilla normalization with the CSA-BN module. By channel and spatially adaptive denormalization, the CSA-BN module can mitigate the spatial mean and variance shifts in each channel in a targeted way. In addition, we also integrate the SLSM-CA layer into our model to capture the long-range correlations explicitly. By introducing dual-branch attention and a feature selection module, the SLSM-CA layer can selectively utilize the multi-scale background information to improve prediction quality. What’s more, it introduces the latent spaces to achieve the low-rank approximations of attention matrices and to reduce computational costs. Extensive quantitative and qualitative evaluations demonstrate the superiority of the proposed method compared with state-of-the-art methods.

AN EFFECTIVE FRAMEWORK FOR AUTOMATIC SEGMENTATION OF HARD EXUDATES IN FUNDUS IMAGES

In this paper, we propose an effective framework to automatically segment hard exudates (HEs) in fundus images. Our framework is based on a coarse-to-fine strategy, as we first get a coarse result allowed of some negative samples, then eliminate the negative samples step by step. In our framework, we make the most of the multi-channel information by employing a boosted soft segmentation algorithm. Additionally, we develop a multi-scale background subtraction method to obtain the coarse segmentation result. After subtracting the optical disc (OD) region from the coarse result, the HEs are extracted by a SVM classifier. The main contributions of this paper are: (1) propose an efficient and robust framework for automatic HEs segmentation; (2) present a boosted soft segmentation algorithm to combine multi-channel information; (3) employ a double ring filter to segment and adjust the OD region. We perform our experiments on the pubic DIARETDB1 dateset, which consists of 89 fundus images. The performance of our algorithm is assessed on both lesion-based criterion and image-based criterion. Our experimental results show that the proposed algorithm is very effective and robust.

Fast super-resolution using an adaptive Wiener filter with robustness to local motion

We present a new adaptive Wiener filter (AWF) super-resolution (SR) algorithm that employs a global background motion model but is also robust to limited local motion. The AWF relies on registration to populate a common high resolution (HR) grid with samples from several frames. A weighted sum of local samples is then used to perform nonuniform interpolation and image restoration simultaneously. To achieve accurate subpixel registration, we employ a global background motion model with relatively few parameters that can be estimated accurately. However, local motion may be present that includes moving objects, motion parallax, or other deviations from the background motion model. In our proposed robust approach, pixels from frames other than the reference that are inconsistent with the background motion model are detected and excluded from populating the HR grid. Here we propose and compare several local motion detection algorithms. We also propose a modified multiscale background registration method that incorporates pixel selection at each scale to minimize the impact of local motion. We demonstrate the efficacy of the new robust SR methods using several datasets, including airborne infrared data with moving vehicles and a ground resolution pattern for objective resolution analysis.

A Block-Based Multi-Scale Background Extraction Algorithm

A Block-Based Multi-Scale Background Extraction Algorithm

A Block-Based Multi-Scale Background Extraction Algorithm

Problem statement: To extract the moving objects, vision-based surveil lance systems subtract the current image from a predefined backgr ound image. The efficiency of these systems mainly depends on accuracy of the extracted backgro und image. It should be able to adapt to the changes continuously. In addition, especially in re al-time applications the time complexity of this adaptation is a critical matter. Approach: In this study, to extract an adaptive background, a combination of blocking and multi-scale methods is presented. Because of being less sensitive to local movements, block-based techniques are proper to control the non-stationary objects' movements, especially in outdoor applications. They can be use ful to reduce the effect of these objects on the extracted background. We also used the blocking method to intelligently select the regions which the temporal filtering has to be applied on. In additio n, an amended multi-scale algorithm is introduced. This algorithm is a hybrid algorithm, a combination of some nonparametric and parametric filters. It uses a nonparametric filter in the spatial domain t o initiate two primary backgrounds. In continue two adapted two-dimensional filters will be used to ext ract the final background. Results: The qualitative and quantitative results of our experiments certify not only the quality of the final extracted background is acceptable, but also its time consump tion is approximately half in compare to the simila r methods. Conclusion: Using Multi scaling filtering and applying the filt ers just to some selected non- overlapped blocks reduce the time consumption of the extracting background algorithm.

An evaluation of optical flow algorithms for background oriented schlieren imaging

The background oriented schlieren method (BOS) allows for accurate flow measurements with a simple experimental configuration. To estimate per-pixel displacement vectors between two images, BOS systems traditionally borrow window-based algorithms from particle image velocimetry. In this paper, we evaluate the performance of more recent optical flow methods in BOS settings. We also analyze the impact of different background patterns, suggesting the use of a pattern with detail at many scales. Experiments with both synthetic and real datasets show that the performance of BOS systems can be significantly improved through a combination of optical flow algorithms and multiscale background.