Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Department of Biomedical EngineeringAlthough photoacoustic endoscopy (PAE) is a great technique with a huge potential in vascular imaging, it yet has some limitation for the clinical translation. Currently, one of the shortcomings of this system is electromagnetic interference (EMI) noise, which decreases signal-to-noise ratio (SNR) and slows down the technology development. The problem can not be simply overcome by increasing the optical pulse energy, unlike in ultrasound endoscopy, due to laser safety requirements. In addition, because PAE requires a wide separation between ultrasound sensor and amplifier, it is a hard task to make PAE system without EMI noise. To accelerate the progress of PAE field development, we accessed the feasibility of deep-learning-based methods for EMI noise removal. We chose four convolutional neural networks (CNN) architectures: U-Net, Segnet, FCN-16s, FCN-8s, and concluded that modified and tuned U-Net architecture suits the best for our application. We also compared deep-learning-based approach to a classical methods of noise removal to prove CNN supremacy. Applying trained and fine-tuned U-Net allowed us to see a tiny capillary mesh-like structures in a successfully denoised 3D vasculature map image, which can be used in future for the angiogenesis studies. For the future work, as we effectively removed noise from PAE images, we also expect that if we increase training dataset, our method can be applied more broadly to many areas of photoacoustic tomography to overcome EMI noise and poor SNR.ope

Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR

Classification of Damaged Road Images Using the Convolutional Neural Network Method

Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images

Reservoir computing for salt-and-pepper noise removal with a small dataset

Department of Mathematical SciencesWe propose a reservoir computing inspiring neural network approach for salt-and-pepper noise removal. Despite the fact that reservoir computing was derived from RNNs dealing with sequential data and that images are not sequential data, our clever reservoir design has made it possible for the reservoir to process image data through a nonlinear image-specific forward operator replacing the linear operator of the multiplication by a randomly chosen input weight matrix. There are two essential advantages of the proposed method. One is that it takes only a small amount of training data as many as a few hundreds of 24??24 images and outperforms most analytic or machine-learning based denoising models for saltand-pepper noise unlike most neural networks requiring a large amount of training data for good results to avoid overfitting, if not the best. Another is that the reconstruction is completely parallel, in that noisy pixels do not communicate with each other, and hence, noise in different pixels can be removed in parallel. Recursive reservoir architecture is also discussed to further improve the reconstruction quality, confirmed by various numerical simulations.clos

Stokes Inversion based on Convolutional Neural Networks

Spectropolarimetric inversions are routinely used in the field of Solar Physics for the extraction of physical information from observations. The application to two-dimensional fields of view often requires the use of supercomputers with parallelized inversion codes. Even in this case, the computing time spent on the process is still very large. Our aim is to develop a new inversion code based on the application of convolutional neural networks that can quickly provide a three-dimensional cube of thermodynamical and magnetic properties from the interpretation of two-dimensional maps of Stokes profiles. We train two different architectures of fully convolutional neural networks. To this end, we use the synthetic Stokes profiles obtained from two snapshots of three-dimensional magneto-hydrodynamic numerical simulations of different structures of the solar atmosphere. We provide an extensive analysis of the new inversion technique, showing that it infers the thermodynamical and magnetic properties with a precision comparable to that of standard inversion techniques. However, it provides several key improvements: our method is around one million times faster, it returns a three-dimensional view of the physical properties of the region of interest in geometrical height, it provides quantities that cannot be obtained otherwise (pressure and Wilson depression) and the inferred properties are decontaminated from the blurring effect of instrumental point spread functions for free. The code is provided for free on a specific repository, with options for training and evaluation.Comment: 18 pages, 14 figures, accepted for publication in Astronomy & Astrophysic

Optimizing the usage of 2D and 3D transformations to improve the BM3D image denoising algorithm

Image denoising is one of the most important pre-processing steps prior to wide range of applications such as image restoration, visual tracking, image segmentation, etc. Numerous studies have been conducted to improve the denoising performance. Block Matching and 3D (BM3D) filtering is the current state-of-the-art algorithm in image denoising and can provide better denoising performance than other existing methods. However, still, there is scope to improve the performance of BM3D. In this thesis, we have pointed out some of the significant aspects of this algorithm which can be improved and also suggested some approaches to get better denoising performance. We have suggested using an adaptive window size rather than the fixed window size. In addition, we have also suggested using gradient image in the blockmatching step to better facilitate the similar patch searching. Experimental results show that our suggested approaches can produce better results than BM3D irrespective of the types of image

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

ACARORUM CATALOGUS IX. Acariformes, Acaridida, Schizoglyphoidea (Schizoglyphidae), Histiostomatoidea (Histiostomatidae, Guanolichidae), Canestrinioidea (Canestriniidae, Chetochelacaridae, Lophonotacaridae, Heterocoptidae), Hemisarcoptoidea (Chaetodactylidae, Hyadesiidae, Algophagidae, Hemisarcoptidae, Carpoglyphidae, Winterschmidtiidae)

The 9th volume of the series Acarorum Catalogus contains lists of mites of 13 families, 225 genera and 1268 species of the superfamilies Schizoglyphoidea, Histiostomatoidea, Canestrinioidea and Hemisarcoptoidea. Most of these mites live on insects or other animals (as parasites, phoretic or commensals), some inhabit rotten plant material, dung or fungi. Mites of the families Chetochelacaridae and Lophonotacaridae are specialised to live with Myriapods (Diplopoda). The peculiar aquatic or intertidal mites of the families Hyadesidae and Algophagidae are also included.Publishe