In this paper, we propose three-dimensional(3D) visualization of objects in heavy scattering media by using peplography and wavelet transform. Conventional haze removal techniques can remove the light haze in the image by using various image processing algorithms or machine learning techniques. However, they may not provide a clear image under heavy scattering media. On the other hand, peplography can visualize the object by detecting ballistic object photons from heavy scattering media. Then, 3D image can be generated by integral imaging. However, it may not visualize 3D object information accurately because of the noise photons from scattering media. Therefore, the image quality for 3D object visualization may be degraded. To solve this problem, we use the discrete wavelet transform in peplography. It can detect the object photon signals from the scattering media and enhance 3D image contrast ratio by using a specific coefficient threshold technique. To prove our method, we carry out the optical experiments and compare with conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal to noise ratio.

Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.

In the image processing method of digital holographic microscopy (DHM), we can obtain a phase information of an object by windowing a sideband in Fourier domain and taking inverse Fourier transform. In this method, it is necessary to window a wide sideband to obtain detailed information on the object. However, since the information of the DC spectrum is widely distributed over the entire range from the center of Fourier domain, the window sideband includes not only phase information but also DC information. For this reason, research on acquiring only the phase information of an object without noise in digital holography is a challenging issue for many researchers. Therefore, in this paper, we propose the use of a windowed sideband array (WiSA) as an image processing method to obtain an accurate three-dimensional (3D) profile of an object without noise in DHM. The proposed method does not affect the neighbor pixels of the filtered pixel but removes noise while maintaining the detail of the object. Thus, a more accurate 3D profile can be obtained compared with the conventional filter. In this paper, we create an ideal comparison target i.e., microspheres for comparison, and verify the effect of the filter through additional experiments using red blood cells.

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.