Imaging Transparent Objects in a Turbid Medium Using a Femtosecond Optical Kerr Gate

A femtosecond optical Kerr gate time-gated ballistic imaging method is demonstrated to image a transparent object in a turbid medium. The shape features of the object are obtained by time-resolved selection of the ballistic photons with different optical path lengths, the thickness distribution of the object is mapped, and the maximum is less than 3.6%. This time-resolved ballistic imaging has potential applications in studying properties of the liquid core in the near field of the fuel spray.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

Transparent Object Reconstruction Based on Compressive Sensing and Super-Resolution Convolutional Neural Network

The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressive sensing(CS)and super-resolution convolutional neural network(SRCNN)techniques are combined to capture transparent objects.With the proposed method,the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction.The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object.However,the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly.The developed algorithm locates the deformities in the resultant images and improves the image quality.Additionally,the inclusion of compressive sensing minimizes the measurements required for reconstruction,thereby reducing image post-processing and hardware requirements during network training.The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index(SSIM)value of 0.2 to 0.53.In this work,we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.

Evaluation of semivariogram features for objectbased image classification

Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

Experimental investigation of ghost imaging of reflective objects with different surface roughness

We present an experimental demonstration of ghost imaging of reflective objects with different surface roughness.The influence of the surface roughness, the transverse size of the test detector, and the reflective angle on the signal-to-noise ratio(SNR) is analyzed by measuring the second-order correlation of the light field based on classical statistical optics. It is shown that the SNR decreases with an increment of the surface roughness and the detector's transverse size or a decrease of the reflective angle. Additionally, the comparative studies between the rough object and the smooth one under the same conditions are also discussed.

Holographic imaging of full-color real-existing three-dimensional objects with computer-generated sequential kinoforms

We propose a computational method for generating sequential kinoforms of real-existing full-color three- dimensional （3D） objects and realizing high-quality 3D imaging. The depth map and color information are obtained using non-contact full-color 3D measurement system based on binocular vision. The obtained full-color 3D data are decomposed into multiple slices with RGB channels. Sequential kinoforms of each channel are calculated and reconstructed using a Fresnel-diffraction-based algorithm called the dynamic- pseudorandom-phase tomographic computer holography （DPP-TCH）. Color dispersion introduced by different wavelengths is well compensated by zero-padding operation in the red and green channels of object slices. Numerical reconstruction results show that the speckle noise and color-dispersion are well suppressed and that high-quality full-color holographic 3D imaging is feasible. The method is useful for improving the 3D image quality in holographic displays with pixelated phase-type spatial light modulators （SLMs）.

Colored object encoding scheme in ghost imaging system using orbital angular momentum

A colored object encoding scheme in a ghost imaging （GI） system using orbital angular momentum is in- vestigated. A colored object is decomposed into three components and then each component is obtained in the idler arm using a multiple grayscale encoding scheme. Afterward, we synthesize the three reconstructed components into a colored image. The scheme is conducted and then presented through numerical simula- tions and experiments. The simulation result shows that the average peak signal-to-noise ratio （PSNR） is at 21.636 for the reconstructed color of the ＂Lena＂ image with 255 gray scales. The experiment also shows that the PSNR is 8.082 for the reconstructed color of the ＂NUPT＂ characters. The successful imaging of colored obiects extends the further use of the GI technique

Nonorthogonal object identification based on ghost imaging

Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method, an object is illuminated by one photon from an entangled pair, and the other one is diffracted into a particular direction by a pre-established multiple-exposure hologram in the idler arm. By the correlation measurements, the nonorthogonal object in the signal arm could be discriminated within a very short time. The constraints for the identification of nonorthogonal objects are presented, which show that the nonorthogonal objects can be discriminated when the overlapping portion between any two objects is less than half of all the objects in the set. The numerical simulations further verify the result.

An effective graph and depth layer based RGB-D image foreground object extraction method

We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To

Big Earth data:disruptive changes in Earth observation data management and analysis?

Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.