• Current Optics and Photonics
• /
• v.4 no.6
• /
• pp.530-539
• /
• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.300-303
• /
• 2015

Digital imaging process has been put to practical use in various application sectors due to a rapid advancement such as memory devices, etc. However, noises are being generated due to various reasons during the image processing and a variety of methods are being studied in order to eliminate these noises. Generally, images are damaged due to a mixed noise having different characteristics. In this paper, a filter algorithm which switches according to the noise types was proposed in order to mitigate the influence of mixed noise included in the image. And using the PSNR as the standard for objective decision making of the improvement effect, it was compared with the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2717-2723
• /
• 2015

Recently, digital image processing is being applied in various areas of broadcasting, communication, computer graphic and medical science. But, degradation of images occurs in the process of digital image acquisition, processing and transmission. Therefore, in order to remove the mixed noise, this paper suggested the image restoration algorithm to process salt and pepper noise with weighted filters according to 4 direction pixel changes after judging the noise and to process AWGN with weighted filters which have individually different characteristics. Regarding the processed results by applying Boat images which were corrupted by salt and pepper noise(P=40%), suggested algorithm showed the improvement by 1.33[dB], 1.41[dB], 0.51[dB] respectively compared with the existing CWMF, AWMF, MMF.

• Journal of Information Processing Systems
• /
• v.19 no.6
• /
• pp.858-869
• /
• 2023

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.214-216
• /
• 2019

Recently, as the use of digital equipment increases in various fields, the importance of image and signal processing is increasing. However, many kinds of noise occur in the digital signal during transmission and reception, and this noise greatly affects the final output of the system. In this paper, we propose an algorithm that effectively restores the image by removing noise according to pixel similarity in a mixed noise environment with impulse noise and AWGN. The proposed algorithm sets the reference value according to the noise type and applies the filtering to pixels similar to the reference value to obtain the final output. Simulation results show that the proposed algorithm has good noise canceling performance and compared with conventional methods using PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.5
• /
• pp.1064-1070
• /
• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.399-402
• /
• 2000

In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between- channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth(LMF), and a smoothing functional is proposed, We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.12
• /
• pp.3718-3726
• /
• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.2
• /
• pp.455-462
• /
• 2015

Image media is used for the internet, computers and digital cameras as part of the core services of multimedia. Digital images can be easily acquired and processed, due to the development of digital home appliances and personal computers' application software. However, image degradation occurs by various external causes in the acquisition, processing and transmitting process of digital images, and its main cause is known to be noise. Therefore, this study proposed and conducted the simulation of image restoration filter algorithm that processes impulse noise and Gaussian noise by applying Lagrange interpolation and spatial weighted method according to distance, respectively. The proposed algorithm improved 8.77[dB], 8.83[dB] and 10.02[dB], respectively, compared to existing A-TMF, AWMF and MMF, as a result of processing by applying the damaged Girl images to impulse noise(P=60%) and Gaussian noise(${\sigma}=10$).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.112-114
• /
• 2014

Generally, images are corrupted by the impulse or AWGN and there are cases where both of these noises are added at once. When it comes to eliminating the noises added to the image, the previous median filter is effective in removing the impulse noise and the average filter is effective for removing AWGN. However, when the complex noises are added, it lacks the noise suppression characteristics, thus in this paper, a non-linear filter algorithm for removing the complex noises was proposed. The simulation results shows the proposed algorithm has excellent de-noising capabilities of compare existing methods.