• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.327-329
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• 2021

Underwater acoustic communication channel is influenced by environmental parameters such as multipath, background noise and scattering. Therefore, a transmitted signal is influenced by the sea surface and the sea bottom boundaries, and a received signal shows a delay spread. These factors create a noise in the image and degrade the quality of underwater acoustic communication. To solve these problems, in this paper, we evaluate the performance of an underwater acoustic communication model using a denoising auto-encoder used for unsupervised learning. Noise images generated by the underwater multipath channel were collected and used as training data. Experimental results were analyzed as a PSNR parameter that expressed the noise ratio of the two images.

• Transactions of the Society of Information Storage Systems
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• v.5 no.1
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• pp.36-40
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• 2009

We describe the experimental realization of image system using wavefront coding in 3-Mega pixel mobile phone camera. We designed aspheric lens to extend the depth of field (DOF) using wavefront coding. In addition, through the aspheric lens and lens barrel manufacturing, we obtained a raw image from a camera module. In our method, the acquired images are restored in the spatial frequency domain using the proposed filter and the spatial frequency response (SFR) is calculated. The proposed filters are composed of image denoising filter using low band pass filter in frequency domain and restoration filter for image restoration. Finally, we achieve an enhanced image by super-resolution image processing. Visual examples are given to demonstrate the performance of the proposed filter.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.46-53
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• 2011

Nonlocal means denoising algorithm is one of the most widely used denoising algorithm. Because it performs well, and the theoretic idea is intuitive and simple. However the conventional nonlocal means algorithm has still some problems such as noise remaining in the denoised flat region and blurring artifacts in the denoised edge and pattern region. Thus many improved algorithms based on nonlocal means have been proposed. In this paper, we proposed new improved nonlocal means denoising algorithm by weight update through weights sorting and newly defined threshold. Updated weights can make weights more refined and definite, and denoising is possible without that artifacts. Experimental results including comparisons with conventional algorithms for various noise levels and test images show the proposed algorithm has a good performance in both visual and quantitative criteria.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.763-768
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• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.965-968
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• 2016

This paper describes a new speckle noise reduction methode on the sonar image using TVG Filtering and PDF wavelet transform. The speckle noise makes the degrading image to discriminate the various object on the ocean bed. The TVG filter removes the speckle noise by gain with observing the results timely and inductively. The experimental result is that speckle noise is reduced to 90 %. Thus the proposed technique leads the mage recognition to be enhanced in the submarine environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.247-250
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• 2005

In numerous occasions, there is need to reconstruct partially occluded facial image. Typical examples include the recognition of criminals whose facial images are captured by surveillance cameras- ln such cases a significant part of the face is occluded making the process of identification extremely difficult, both for automatic face recognition systems and human observers. To overcome these difficulties, we consider the application of Kernel PCA-based denoising method to partially occluded facial image in this paper.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.163-165
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• 2019

The task of image denoising is gaining popularity in the computer vision research field. Its main objective of restoring the sharp image from given noisy input is demanded in all image processing procedure. In this work, we treat the process of residual hair removal on faces images similar to the task of image denoising. In particular, our method removes the residual hair that presents on the frontal or profile face images and in-paints it with the relevant skin color. To achieve this objective, we employ a deep neural network that able to perform both tasks in one time. Furthermore, simple technic of residual hair color augmentation is introduced to increase the number of training data. This approach is beneficial for improving the robustness of the network. Finally, we show that the experimental results demonstrate the superiority of our network in both quantitative and qualitative performances.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.