Hyun-Woo Kim’s research while affiliated with Kyushu Institute of Technology and other places

Digital Holographic Microscopy (DHM) is a method of converting hologram images into three-dimensional (3D) images by image processing, which enables us to obtain the detailed shapes of the objects to be observed. Three-dimensional imaging of the microscopic objects by DHM can contribute to the early diagnosis and the detection of the diseases in the medical field by observing the shape of the cells. DHM requires several experimental components. One of them is the laser, which is a problem because its high power may cause the deformation and the destruction of the cells and the death of the microorganisms. Since the greatest advantage of DHM is the detailed geometrical information of the object by 3D measurement, the loss of such information is a serious problem. To solve this problem, a Neutral Density (ND) filter has been used to reduce power after the laser irradiation. However, the image acquired by the image sensor becomes too dark to obtain sufficient information, and the effect of noise increased due to the decrease in the amount of light. Therefore, in this paper, we propose the Frame-Stacking Method (FSM) for dark DHM for reproducing 3D profiles that enable us to observe the shape of the objects from the images taken in low-power environments when the power is reduced. The proposed method realizes highly accurate 3D profiles by the frame decomposition of the low-power videos into images and superimposing and rescaling the obtained low-power images. On the other hand, the continuous irradiation of the laser beam for a long period may destroy the shape of the cells and the death of the microorganisms. Therefore, we conducted experiments to investigate the relationship between the number of superimposed images corresponding to the irradiation time and the 3D profile, as well as the characteristics of the power and the 3D profile.

In this paper, we propose a new method for three-dimensional (3D) visualization that proportionally estimates the number of photons in the background and the object under photon-starved conditions. Photon-counting integral imaging is one of the techniques for 3D image visualization under photon-starved conditions. However, conventional photon-counting integral imaging has the problem that a random noise is generated in the background of the image by estimating the same number of photons in entire areas of images. On the other hand, our proposed method reduces the random noise by estimating the proportional number of photons in the background and the object. In addition, the spatial overlaps have been applied to the space where photons overlap to obtain the enhanced 3D images. To demonstrate the feasibility of our proposed method, we conducted optical experiments and calculated the performance metrics such as normalized cross-correlation, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). For SSIM of 3D visualization results by our proposed method and conventional method, our proposed method achieves about 3.42 times higher SSIM than conventional method. Therefore, our proposed method can obtain better 3D visualization of objects than conventional photon-counting integral imaging methods under photon-starved conditions.

In this paper, we propose automation of estimating scattering media information in peplography using singular value decomposition (SVD) and discrete cosine transform (DCT). Conventional scattering media-removal methods reduce light scattering in images utilizing a variety of image-processing techniques and machine learning algorithms. However, under conditions of heavy scattering media, they may not clearly visualize the object information. Peplography has been proposed as a solution to this problem. Peplography is capable of visualizing the object information by estimating the scattering media information and detecting the ballistic photons from heavy scattering media. Following that, 3D information can be obtained by integral imaging. However, it is difficult to apply this method to real-world situations since the process of scattering media estimation in peplography is not automated. To overcome this problem, we use automatic scattering media-estimation methods using SVD and DCT. They can estimate the scattering media information automatically by truncating the singular value matrix and Gaussian low-pass filter in the frequency domain. To evaluate our proposed method, we implement the experiment with two different conditions and compare the result image with the conventional method using metrics such as structural similarity (SSIM), feature similarity (FSIMc), gradient magnitude similarity deviation (GMSD), and learned perceptual image path similarity (LPIPS).

Digital Holographic Microscopy (DHM) is a technique that uses the phase information of light to generate a three-dimensional (3D) profile of an object. Recently, it has been utilized in various fields such as disease diagnosis and research on microorganisms. In the process in DHM, a narrow region around one of the sidebands from the frequency domain is windowed to avoid noise caused by the direct current (DC) term. However, it may not obtain the high-frequency information about the object. On the other hand, windowing a wide region increases the noise caused by the DC term, and generates the noise in the 3D profile. To solve this trade-off, we propose a noise reduction method using Kalman filter. From the recorded hologram image, we can create the frequency domain. It obtains multiple windowed sidebands centered on multiple pixels at random from the frequency domain. This creates a group of data in which noise is generated randomly. This is regarded as frequency series data, and Kalman filtering is performed. This method can reduce the noise caused by the DC term while acquiring high-frequency information. In addition, this method has the advantage that only one image is needed for frequency series data in the Kalman filter. The effectiveness of the proposed method is verified by comparison with conventional filtering methods and general image processing methods. The validation results prove the usefulness of the proposed method, and the proposed method is expected to have a significant effect on improving the accuracy of disease diagnosis techniques using DHM.

Peplography is a technology for removing scattering media such as fog and smoke. However, Peplography only removes scattering media, and decisions about the images are made by humans. Therefore, there are still many improvements to be made in terms of system automation. In this paper, we combine Peplography with You Only Look Once (YOLO) to attempt object detection under scattering medium conditions. In addition, images reconstructed by Peplography have different characteristics from normal images. Therefore, by applying Peplography to the training images, we attempt to learn the image characteristics of Peplography and improve the detection accuracy. Also, when considering autonomous driving in foggy conditions or rescue systems at the scene of a fire, three-dimensional (3D) information such as the distance to the vehicle in front and the person in need of rescue is also necessary. Furthermore, we apply a stereo camera to this algorithm to achieve 3D object position and distance detection under scattering media conditions. In addition, when estimating the scattering medium in Peplography, it is important to specify the processing area, otherwise the scattering medium will not be removed properly. Therefore, we construct a system that continuously improves processing by estimating the size of the object in object detection and successively changing the area range using the estimated value. As a result, the PSNR result by our proposed method is better than the PSNR by the conventional Peplography process. The distance estimation and the object detection are also verified to be accurate, recording values of 0.989 for precision and 0.573 for recall. When the proposed system is applied, it is expected to have a significant impact on the stability of autonomous driving technology and the safety of life rescue at fire scenes.

Dendropanax morbifera Leveille is a traditional medicine used to treat migraine headache and dysmenorrhea. In this study, three polyacetylenes, methyl (10E,9R,16R)-16-acetoxy-9-hydroxyoctadeca-10,17-dien-12,14-diynoate (1), methyl (10E,9R,16S)-9,16-dihydroxyoctadeca-10-en-12,14-diynoate (2), and methyl (10Z,9R,16S)-9,16-dihydroxyoctadeca-10,17-dien-12,14-diynoate (3), were isolated from the aerial parts of D. morbifera, together with seven known compounds (4–10). Importantly, the isolates (6 and 8) were found in the family Araliaceae for the first time in this study. Compounds 1−10 were evaluated for their binding affinity to AMPK and CTSS receptors using in silico docking simulations. Only compound 7 increased the protein expression levels of PPAR-α, Sirt1, and AMPK when administered to HepG2 cells as a PPAR-α agonist. On the other hand, 7 did not produce any significant reduction in CTSS activity. This study could pave the way for the discovery of novel treatments from D. morbifera targeting PPAR-α and AMPK.