A stabilized least-squares imaging condition with structure constraints

Conventional shot-gather migration uses a cross-correlation imaging condition proposed by Clarebout （1971）, which cannot preserve imaging amplitudes. The deconvolution imaging condition can improve the imaging amplitude and compensate for illumination. However, the deconvolution imaging condition introduces instability issues. The least-squares imaging condition first computes the sum of the cross-correlation of the forward and backward wavefields over all frequencies and sources, and then divides the result by the total energy of the forward wavefield. Therefore, the least-squares imaging condition is more stable than the classic imaging condition. However, the least-squares imaging condition cannot provide accurate results in areas where the illumination is very poor and unbalanced. To stabilize the least-squares imaging condition and balance the imaging amplitude, we propose a novel imaging condition with structure constraints that is based on the least-squares imaging condition. Our novel imaging condition uses a plane wave construction that constrains the imaging result to be smooth along geological structure boundaries in the inversion frame. The proposed imaging condition improves the stability of the imaging condition and balances the imaging amplitude. The proposed condition is applied to two examples, the horizontal layered model and the Sigsbee 2A model. These tests show that, in comparison to the damped least-squares imaging condition, the stabilized least-squares imaging condition with structure constraints improves illumination stability and balance, makes events more consecutive, adjusts the amplitude of the depth layers where the illumination is poor and unbalanced, suppresses imaging artifacts, and is conducive to amplitude preserving imaging of deep layers.

Reverse time migration based on normalized wavefield decomposition imaging condition

With the increasing complexity of prospecting objectives,reverse time migration( RTM) has attracted more and more attention due to its outstanding imaging quality. RTM is based on two-way wave equation,so it can avoid the limits of angle in traditional one-way wave equation migration,image reverse branch,prism waves and multi-reflected wave precisely and obtain accurate dynamic information. However,the huge demands for storage and computation as well as low frequency noises restrict its wide application. The normalized cross-correlation imaging conditions based on wave field decomposition are derived from traditional cross-correlation imaging condition,and it can eliminate the low-frequency noises effectively and improve the imaging resolution. The practical procedure includes separating source and receiver wave field into one-way components respectively,and conducting cross-correlation imaging condition to the post-separated wave field. In this way,the resolution and precision of the imaging result will be promoted greatly.

Elastic-energy imaging condition based on energy-flow vector

Elastic reverse-time migration can effectively deal with multicomponent seismic data in which the imaging condition based on energy norm can extract the scalar-imaging result from multicomponent data.However,the energy cross-correlation imaging condition characterized by particle velocity and stress suffers from the problem of overdependence on the background elastic parameters.Therefore,we characterize the elastic-wave energy using the energy-flow vector,which is equal to the energy density,without background elastic parameters.According to the source and receiver wave fields,we propose an imaging energyflow vector and an elastic-wave energy imaging condition.Under the assumption of a planewave solution,the backscattering suppression is verified.The numerical simulations show that the elastic-energy imaging condition can obtain the energy image without backscattering.Compared with the cross-correlation imaging conditions in a vector-based wave field,the proposed imaging condition can eliminate the dependence on the background elastic parameters and effectively process seabed multicomponent data,which are conducive to further providing an interpretation of marine geological structures.

3D prestack reverse time migration of ground penetrating radar data based on the normalized correlation imaging condition

The reverse time migration(RTM)of ground penetrating radar(GPR)is usually implemented in its two-dimensional(2D)form,due to huge computational cost.However,2D RTM algorithm is difficult to focus the scattering signal and produce a high precision subsurface image when the object is buried in a complicated subsurface environment.To better handle the multi-off set GPR data,we propose a three-dimensional(3D)prestack RTM algorithm.The high-order fi nite diff erence time domian(FDTD)method,with the accuracy of eighth-order in space and second-order in time,is applied to simulate the forward and backward extrapolation electromagnetic fi elds.In addition,we use the normalized correlation imaging condition to obtain pre-stack RTM result and the Laplace fi lter to suppress the low frequency noise generated during the correlation process.The numerical test of 3D simulated GPR data demonstrated that 3D RTM image shows excellent coincidence with the true model.Compared with 2D RTM image,the 3D RTM image can more clearly and accurately refl ect the 3D spatial distribution of the target,and the resolution of the imaging results is far better.Furthermore,the application of observed GPR data further validates the eff ectiveness of the proposed 3D GPR RTM algorithm,and its fi nal image can more reliably guide the subsequent interpretation.

AB010. The effect of visual conditioning on cortical map plasticity:a wide-field calcium imaging study

Background:Visual conditioning can refine the response of neurons in the visual cortex and higher visual and cognitive processing of a presented stimulus.This process results in increased sensitivity for that stimulus.The development of new optical imaging technology in the field of neuroscience has led to important advances,notably to better define the functional organization and plasticity of visual areas.The objective of this project is to determine the effect of daily visual conditioning with an oblique sinusoidal grating on the distribution and amplitude of cortical responses.For this,we use wide-field calcium imaging on awake mice,allowing for the observation of responses to a stimulus throughout the entire cortex in real time.Methods:C57BL/6 mice,expressing the GCaMP6s calcium reporter gene,are used to longitudinally measure neuronal activity via wide-field calcium imaging.Spontaneous activity at rest,as well as cortical responses to visual stimuli consisting of sinusoidal networks with orientation(0,30°,60°and 90°),spatial frequency(0.03,0.12,0.24 and 0.48 cpd)and contrast(100%,75%and 50%)variables are recorded to establish cortical maps,as well as tuning curves.Subsequently,the baseline function of the cortex,as well as the cortical representation of visual stimulation(30°or 90°,0.03 cpd and a contrast of 50%,75%and 100%)are studied in the animal before,during,and after daily monocular conditioning,consisting of a specific sinusoidal network(30°,0.03 cpd and 100%)over a period of 7 days.The variations in intensity and activation specificity of various visual cortical areas are calculated according to the visual conditioning and compared to an orientation stimulus for which the animal has not been conditioned(90°).Results:The cortical activation curves show a greater sensitivity of response for stimuli having horizontal or vertical gratings(0 and 90°)than for oblique gratings(30°and 60°)at low spatial frequencies(0,0.3 and 0.12 cpd).However,this trend does not occur with high spatial frequencies(0.24 and 0.48 cpd).Finally,although the intensity of activation varies in a way that is not proportional to the contrast of the stimulation,it would have no influence on the perception of the orientation of the stimuli.Conditioning at a 30°stimulus results in greater activation of the primary visual cortex and some extra-striate visual areas,as well as greater amplification of the ipsilateral cortical responses to the presentation of the visual stimuli.Conclusions:In conclusion,the results demonstrate that visual conditioning would allow for plasticity and consolidation of higher visual pathways.

An improved reverse time migration for subsurface imaging over complex geological structures:A numerical study

In seismic exploration,it is a critical task to image and interpret different seismic signatures over complex geology due to strong lateral velocity contrast,steep reflectors,overburden strata and dipping flanks.To understand the behavior of these seismic signatures,nowadays Reverse Time Migration(RTM)technique is used extensively by the oil&gas industries.During the extrapolation phase of RTM,the source wavefield needs to be saved,which needs high storage memory and large computing time.These two are the main obstacles of RTM for production use.In order to overcome these disadvantages,in this study,a second-generation improved RTM technique is proposed.In this improved form,a shift operator is introduced at the time of imaging condition of RTM algorithm which is performed automatically both in space and time domain.This effort is made to produce a better-quality image by minimizing the computational time as well as numerical artefacts.The proposed method is applied over various benchmark models and validated by implementing over one field data set from the Jaisalmer Basin,India.From the analysis,it is observed that the method consumes a minimum of 45%less storage space and reduce the execution time by 20%,as compared to conventional RTM.The proposed RTM is found to work efficiently in comparison to the conventional RTM both in terms of imaging quality and minimization of numerical artefacts for all the benchmark models as well as field data.

Exploiting PLSA model and conditional random field for refining image annotation

This paper presents a new method for refining image annotation by integrating probabilistic la- tent semantic analysis （PLSA） with conditional random field （CRF）. First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores, and then model semantic relationship among the candidate annotations by leveraging conditional ran- dom field. In CRF, the confidence scores generated lay the PLSA model and the Fliekr distance be- tween pairwise candidate annotations are considered as local evidences and contextual potentials re- spectively. The novelty of our method mainly lies in two aspects ： exploiting PLSA to predict a candi- date set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation. To demonstrate the effectiveness of the method proposed in this paper, an experiment is conducted on the standard Corel dataset and its re- sults are ＇compared favorably with several state-of-the-art approaches.

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

Optimization of image capturing method of wear particles for condition diagnosis of machine parts

Wear particles are inevitably occurred from moving parts, such as a piston-cylinder made from steel or hybrid materials. And a durability of these parts must be evaluated. The wear particle analysis has been known as a very effective method to foreknow and decide a moving situation and a damage of machine parts by using the digital computer image processing. But it is not laid down to calculate shape parameters of wear particle and wear volume. In order to apply image processing method in a durability evaluation of machine parts, it needs to verify the reliability of the calculated data by the image processing and to lay down the number of images and the amount of wear particles in one image. In this work, the lubricated friction experiment was carried out in order to establish the optimum image capture with the 1045 specimen under experiment condition. The wear particle data were calculated differently according to the number of image and the amount of wear particle in one image. The results show that capturing conditions need to be more than 140 wear particles in one image and over 40 images for the reliable data. Thus, the capturing method of wear particles images was optimized for condition diagnosis of machine moving parts.

An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.

Assessment of Cork Oak Decline Using Digital Multispectral Imagery in Relation with in Situ Crown Condition

Cork oak in Maamora forest is experiencing the dieback phenomenon. The evaluation of the latter in this forest has gained the importance over time and with the solicitation of managers to objectify its phytosanitary situation. Aiming at prioritizing management actions, remote sensing seems to be an effective tool to inquire about stands’ health conditions and their evolution. To this end, this study aims at mapping and validating health status of cork oak stands in Maamora. Sentinel 2 images in 2015 and 2020 were processed to calculate the differential normalized difference water index (NDWI), revealing vegetation moisture variation caused by drought. A statistical method based on thresholds was used to map cork oak dieback stands, those with no changes and those recovered. Results have shown that 54.63% of cork oak in Maamora forest have not changed in terms of phytosanitary situation between 2015 and 2020, 31.10% of oak stands are afflicted by a slight decline and 12.97% by a severe decline. Areas with slight or strong recovery remain minimal and represent 1.04% and 0.25% respectively. Ground data indicated that the map generated displayed a good distinction between stands severely and slightly declined with a global accuracy of 66.66%. Therefore, further research elaborating an advanced vegetation index reflecting the various factors of dieback would be of much importance.

Image Rain Removal Using Conditional Generative Networks Incorporating

The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.

Migration scheme for imaging offset VSP data within local phase space

The imaging of offset VSP data in local phase space can improve the image of the subsurface structure near the well.In this paper,we present a migration scheme for imaging VSP data in a local phase space,which uses the Gabor-Daubechies tight framebased extrapolator（G-D extrapolator） and its high-frequency asymptotic expansion to extrapolate wavefields and also delineates an improved correlation imaging condition in the local angle domain.The results for migrating synthetic and real VSP data demonstrate that the application of the high-frequency G-D extrapolator asymptotic expansion can effectively decrease computational complexity.The local angle domain correlation imaging condition can be used to weaken migration artifacts without increasing computation.

Narrow-band imaging with magnifying endoscopy is accurate for detecting gastric intestinal metaplasia

AIM:To investigate the predictive value of narrowband imaging with magnifying endoscopy (NBI-ME) for identifying gastric intestinal metaplasia (GIM) in unselected patients. METHODS:We prospectively evaluated consecutive patients undergoing upper endoscopy for various indications, such as epigastric discomfort/pain, anaemia, gastro-oesophageal reflux disease, suspicion of peptic ulcer disease, or chronic liver diseases. Patients underwent NBI-ME, which was performed by three blinded, experienced endoscopists. In addition, five biopsies (2 antrum, 1 angulus, and 2 corpus) were taken and examined by two pathologists unaware of the endoscopic findings to determine the presence or absence of GIM. The correlation between light blue crest (LBC) appearance and histology was measured. Moreover, we quantified the degree of LBC appearance as less than 20% (+), 20%-80% (++) and more than 80% (+++) of an image field, and the semiquantitative evaluation of LBC appearance was correlated with IM percentage from the histological findings. RESULTS:We enrolled 100 (58 F/42 M) patients who were mainly referred for gastro-esophageal reflux disease/dyspepsia (46%), cancer screening/anaemia (34%), chronic liver disease (9%), and suspected celiac disease (6%); the remaining patients were referred for other indications. The prevalence of Helicobacter pylori (H. pylori ) infection detected from the biopsies was 31%, while 67% of the patients used proton pump inhibitors. LBCs were found in the antrum of 33 patients (33%); 20 of the cases were classified as LBC+, 9 as LBC++, and 4 as LBC+++. LBCs were found in the gastric body of 6 patients (6%), with 5 of them also having LBCs in the antrum. The correlation between the appearance of LBCs and histological GIM was good, with a sensitivity of 80% (95%CI:67-92), a specificity of 96% (95%CI:93-99), a positive predictive value of 84% (95%CI:73-96), a negative predictive value of 95% (95%CI:92-98), and an accuracy of 93% (95%CI:90-97). The NBI-ME examination overlooked GIM in 8 cases, but the GIM was less than 5% in 7 of the cases. Moreover, in the 6 false positive cases, the histological examination showed the presence of reactive gastropathy (4 cases) or H. pylori active chronic gastritis (2 cases). The semiquantitative correlation between the rate of LBC appearance and the percentage of GIM was 79% (P < 0.01). CONCLUSION:NBI-ME achieved good sensitivity and specificity in recognising GIM in an unselected population. In routine clinical practice, this technique can reliably target gastric biopsies.

Diffusion tensor imaging with multiple diffusion-weighted gradient directions

Diffusion tensor MRI （DT-MRI or DTI） is emerging as an important non-invasive technology for elucidating intemal brain structures. It has recently been utilized to diagnose a series of diseases that affect the integrity of neural systems to provide a basis for neuroregenerative studies. Results from the present study suggested that neural tissue is reconstructed with multiple diffusion-weighted gradient directions DTI, which varies from traditional imaging methods that utilize 6 gradient directions. Simultaneously, the diffusion tensor matrix is obtained by multiple linear regressions from an equation of echo signal intensity. The condition number value and standard deviation of fractional anisotropy for each scheme can be used to evaluate image quality. Results demonstrated that increasing gradient direction to some extent resulted in improved effects. Therefore, the traditional 6 and 15 directions should not be considered optimal scan protocols for clinical DTI application. In a scheme with 20 directions, the condition number and standard deviation of fractional anisotropy of the encoding gradients matrix were significantly reduced, and resulted in more clearly and accurately displayed neural tissue. Results demonstrated that the scheme with 20 diffusion gradient directions provided better accuracy of structural renderings and could be an optimal scan protocol for clinical DTI application.

Large-scale conditions of Tibet Plateau vortex departure

Based on the circumfluence situation of the out- and in-Tibet Plateau Vortex (TPV) from 1998–2004 and its weather-influencing system,multiple synthesized physical fields in the middle–upper troposphere of the out- and in-TPV are computationally analyzed by using re-analysis data from National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) of United States.Our research shows that the departure of TPV is caused by the mutual effects among the weather systems in Westerlies and in the subtropical area,within the middle and the upper troposphere.This paper describes the large-scale meteorological condition and the physics image of the departure of TPV,and the main differences among the large-scale conditions for all types of TPVs.This study could be used as the scientific basis for predicting the torrential rain and the floods caused by the TPV departure.

Experimental Study on Hydrodynamics of L-type Podded Propulsor in Straight-ahead Motion and Off-Design Conditions

Experimental tests were conducted to evaluate the hydrodynamic performance of an L-type podded propulsor in straight-ahead motion and off-design conditions using an open-water measuring instrument developed by the authors for podded propulsors, a ship model towing tank, and under water particle image velocimetry （PIV） measurement systems. Under the three types of conditions, the main parameters of an L-type podded propulsor were measured, including the propeller thrust and torque, as well as the thrust, side force, and moment of the whole pod unit.In addition, the flow field on the section between the propeller and the strut was analyzed. Experimental results demonstrate that the dynamic azimuthing rate and direction and the turning direction affect the forces on the propeller and the whole pod unit. Forces are asymmetrically distributed between the left and right azimuthing directions because of the effect of propeller rotation. The findings of this study provide a foundation for further research on L-type podded propulsors.

A FAST CONVERGING SPARSE RECONSTRUCTION ALGORITHM IN GHOST IMAGING

A fast converging sparse reconstruction algorithm in ghost imaging is presented. It utilizes total variation regularization and its formulation is based on the Karush-Kuhn-Tucker (KKT) theorem in the theory of convex optimization. Tests using experimental data show that, compared with the algorithm of Gradient Projection for Sparse Reconstruction (GPSR), the proposed algorithm yields better results with less computation work.

Endoscopic advances in the management of gastric cancer and premalignant gastric conditions

Gastric cancer is the fifth most common cancer and in 2018,it was the third most common cause of cancer-related deaths worldwide.Endoscopic advances continue to be made for the diagnosis and management of both early gastric cancer and premalignant gastric conditions.In this review,we discuss the epidemiology and risk factors of gastric cancer and emphasize the differences in early vs latestage gastric cancer outcomes.We then discuss endoscopic advances in the diagnosis of early gastric cancer and premalignant gastric lesions.This includes the implementation of different imaging modalities such as narrow-band imaging,chromoendoscopy,confocal laser endomicroscopy,and other experimental techniques.We also discuss the use of endoscopic ultrasound in the diagnosis and staging of early gastric cancer.We then discuss the endoscopic advances made in the treatment of these conditions,including endoscopic mucosal resection,endoscopic submucosal dissection,and hybrid techniques such as laparoscopic endoscopic cooperative surgery.Finally,we comment on the current suggested recommendations for surveillance of both gastric cancer and its premalignant conditions.