• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.227-232
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• 2013

The pixels used in a digital X-ray detector have different sensitivities and offset values. A non-uniform image is consequently obtained. Flat-field correction was introduced to resolve this problem and carried out image preprocessing in a digital imaging system. Nevertheless, the non-uniform images caused by several reasons have been being occasionally acquired. In this study, the non-uniform images acquired in digital imaging systems were applied to flat-field correction, and NPSs were calculated and analyzed with those images before and after correction. It was confirmed that low frequency noise were effectively eliminated.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.159-169
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• 2017

The Satellite Electro-Optic Payload System needsspecial requirements with the conditions of limited power consumption and the space environment of solar radiation. The acquired image quality should be mainly depend on the GSD (Ground Sampled Distance), SNR (Signal to Noise Ratio), and MTF (Modulation Transfer Function). On the well-manufactured sensor level, the thermal noise is removed on ASP (Analog Signal Processing) using the CDS (Corrective Double Sampling); the noise signal from the image sensor can be reduced from the offset signals based on the pre-pixels and the dark-pixels. The non-uniformity shall be corrected with gain, offset, and correction parameter of the image sensor pixel characteristic on the sensor control system. This paper describes the SNR enhancement method of the satellite EOS payload using the mentioned noise remove processes on the system design and operation, which is verified by tests and simulations.

• Journal of Korea Multimedia Society
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• v.17 no.11
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• pp.1279-1285
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• 2014

In this paper, we propose on image encryption method which uses NC(Non-linear Cycle) and 2D CAT(Two-Dimensional Cellular Automata Transform) in sequence to encrypt medical images. In terms of the methodology, we use NC to generate a pseudo noise sequence equal to the size of the original image. We then conduct an XOR operation of the generated sequence with the original image to conduct level 1 NC encryption. Then we set the proper Gateway Values to generate the 2D CAT basis functions. We multiply the generated basis functions by the altered NC encryption image to conduct the 2nd level 2D CAT encryption. Finally, we verify that the proposed method is efficient and extremely safe by conducting an analysis of the key spatial and sensitivity analysis of pixels.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.155-158
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• 2000

In this paper, the quantization noise in block-based video coding is analyzed, and a post-processing method based on the analysis is presented for reducing the quantization noise by using a wavelet transform(WT). In the proposed method, the quantization noise is considered as the sum of a blocking noise expressed as a deterministic profile and the random remainder noise. Each noise is removed in a viewpoint of image restoration using a 1-D WT, which yields a regularized differentiation. The blocking noise first is reduced by weakening the strength of each blocking noise component that appears as an impulse in the first scale wavelet domain. The impulse strength estimation is performed using median filter, quantization parameter(QP), and local activity. The remainder noise, which is considered as a white noise at non-edge pixels, then is reduced by soft-thresholding. The experimental results show that the proposed method yields better performance in terms if subjective quality as well as PSNR performance over VM post-filter in MPEG-4 for all test sequences of various compression ratios. We also present a fast post-processing in spatial domain equivalent to that in wavelet domain for real-time application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1955-1960
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• 2016

Currently, with the rapid development in digital era, the image equipment related to multi-media is becoming commercialized. However, in the process of transmitting image data, deterioration occurs due to various causes, and the most representative deterioration is salt and pepper noise. There are many methods of eliminating salt and pepper noise such as SWMF, RSIF, MNRF, which are rather insufficient in eliminating noise in high-density slat and pepper noise environment. Therefore, in order to eliminate salt and pepper noise, this thesis proposes an algorithm by first judging the noise, and when the center pixel value is non-noise, the original pixel is preserved, and when it is noise, the partial mask is subdivided into 4 directions to apply cubic spline interpolation to the direction with most non-noise pixels. Also, for the objective judgement, it was compared to existing methods, and the PSNR(peak signal to nise ratio) was set as the judgement standard.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.409-411
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• 2016

Salt and pepper noise is caused by various causes such as camera malfunction, storage media memory error, and transmission channel error. Representative filters to remove salt and pepper noise include SMF(standard median filter), CWMF(center weighted median filter), and AMF(adaptive median filter). However previous filters have inadequate noise removal characteristics in high density salt-and-pepper noise environment. Therefore the study suggested a composite filter which, through noise evaluation, preserves original pixels when the central pixel is non-noise, and uses spatial weighted value mask and median when there is noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.230-236
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• 2015

In these digital times, the image signal processing is being used in various areas like vehicle recognition, security, and robotics. Generally, the image deterioration occurs by salt & pepper noise in the procedures of image transmission, storage, and processing. Methods to remove this noise are SMF, CWMF, and SWMF and these methods have few unsatisfactory noise reduction characteristics in salt & pepper noise environment. Therefore, in order to mitigate salt & pepper noise which is added in the image, this study suggested an algorithm which subdivides the masks in the image into four areas and processes using non-noise pixel numbers in each area. Additionally, in order to prove the excellence of the proposed algorithm, relevant performances were compared with existing methods using PSNR.

• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.496-507
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• 2006

A hybrid approach integrating both high-resolution and hyperspectral data sets was used to map vegetation cover of a coastal lowland area in the Hawaii Volcanoes National Park. Three common grass species (broomsedge, natal redtop, and pili) and other non-grass species, primarily shrubs, were focused in the study. A 3-step, hybrid approach, combining an unsupervised and a supervised classification schemes, was applied to the vegetation mapping. First, the IKONOS 1-m high-resolution data were classified to create a binary image (vegetated vs. non--vegetated) and converted to 20-meter resolution percent cover vegetation data to match AVIRIS data pixels. Second, the minimum noise fraction (MNF) transformation was used to extract a coherent dimensionality from the original AVIRIS data. Since the grasses and shubs were sparsely distributed and most image pixels were intermingled with lava surfaces, the reflectance component of lava was filtered out with a binary fractional cover analysis assuming that tile total reflectance of a pixel was a linear combination of the reflectance spectra of vegetation and the lava surface. Finally, a supervised approach was used to classify the plant species based on tile maximum likelihood algorithm.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1108-1118
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• 2019

Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FM-NLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-of-the-art NLM-based denoising algorithms.