• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2514-2526
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• 2013

This paper presents a detection method for least significant bit matching revisited (LSBMR) steganography. Previous research shows that the adjacent pixels of natural images are highly correlated and the value 0 appears most frequently in pixel difference. Considering that the message embedding process of LSBMR steganography has a weighted-smoothing effect on the distribution of pixel difference, the frequency of the occurrence of value 0 in pixel difference changes most significantly whereas other values approximately remain unchanged during message embedding. By analyzing the effect of LSBMR steganography on pixel difference distribution, an equation is deduced to estimate the frequency of difference value 0 using the frequencies of difference values 1 and 2. The sum of the ratio of the estimated value to the actual value as well as the ratio of the frequency of difference value 1 to difference value 0 is used as the steganalytic detector. Experimental results show that the proposed method can effectively detect LSBMR steganography and can outperform previous proposed methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.573-577
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• 2016

In this paper, reversible data embedding scheme was proposed using the locality of image and the adjacent pixel difference sequence. Generally, locality exists in natural image. The proposed scheme increases the amount of embedding data and enables data embedding at various levels by applying a technique of predicting adjacent pixel values using image locality to an existing technique APD(Adjacent Pixel Difference). The experimental results show that the proposed scheme is very useful for reversible data embedding.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.261-270
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• 2019

In order to protect secret digital documents against vulnerabilities while communicating, steganography algorithms are applied. It protects a digital file from unauthorized access by hiding the entire content. Pixel-value-difference being a method from spatial domain steganography utilizes the difference gap between neighbor pixels to fulfill the same. The proposed approach is a block-wise embedding process where blocks of variable size are chosen from the cover image, therefore, a stream of secret digital contents is hidden. Least significant bit (LSB) substitution method is applied as an adaptive mechanism and optimal pixel adjustment process (OPAP) is used to minimize the error rate. The proposed application succeeds to maintain good hiding capacity and better signal-to-noise ratio when compared against other existing methods. Any means of digital communication specially e-Governance applications could be highly benefited from this approach.

• Journal of Information Technology Applications and Management
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• v.10 no.3
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• pp.45-54
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• 2003

Security system with web camera remarkably has been developed at an Internet era. Using transmitted images from remote camera, the system can recognize current situation and take a proper action through web. Existing motion detection methods use simply difference image, background image techniques or block matching algorithm which establish initial block by set search area and find similar block. But these methods are difficult to detect exact motion because of useless noise. In this paper, the proposed method is updating changed background image as much as $N{\times}M$pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect motion by computing fixed distance pixel instead of operate all pixel.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.21-28
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• 2015

The essential idea of de-noising is referring to neighboring pixels of a center pixel to be updated. Conventional adaptive de-noising filters use local statistics, i.e., mean and variance, of neighboring pixels including the center pixel. The drawback of adaptive de-noising filters is that their performance becomes low when edges are contained in neighboring pixels, while anisotropic diffusion de-noising filters remove adaptively noises and preserve edges considering intensity difference between neighboring pixel and the center pixel. The anisotropic diffusion de-noising filters, however, use only intensity difference between neighboring pixels and the center pixel, i.e., local statistics of neighboring pixels and the center pixel are not considered. We propose a new connectivity function of two adjacent pixels using statistics of neighboring pixels and apply connectivity function to diffusion coefficient. Experimental results using an aerial image corrupted by uniform and Gaussian noises showed that the proposed algorithm removed more efficiently noises than conventional diffusion filter and median filter.

• Current Optics and Photonics
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• v.4 no.5
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• pp.428-433
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• 2020

Recently, the on-chip autofocus (AF) function has become essential to the CMOS image sensor. An auto-focus usually operates using phase detection of the photocurrent difference from a pair of AF pixels that have focused or defocused. However, the phase-detection method requires a pair of AF pixels for comparison of readout. Therefore, the pixel variation may reduce AF performance. In this paper, we propose a color-selective AF pixel with a plasmonic nanostructure in a 0.9 μ㎡ pixel. The suggested AF pixel requires one pixel for AF function. The plasmonic nanostructure uses metal-insulator-metal (MIM) stack arrays instead of a color filter (CF). The color filters are formed at the subwavelength, and they transmit the specific wavelength of light according to the stack period and incident angles. For the optical analysis of the pixel, a finite-difference time-domain (FDTD) simulation was conducted. The analysis showed that the MIM stack arrays in the pixels perform as an AF pixel. As the primary metric of AF performance, the resulting AF contrasts are 1.8 for the red pixels, 1.6 for green, and 1.5 blue. Based on the simulation results, we confirmed the autofocusing performance of the MIM stack arrays.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.449-454
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• 2020

We propose an improved de-interlacing method that converts the interlaced images into the progressive images by one field. In the first, Inter-pixel values are calculated by applying Newton's forward difference, backward difference interpolation from upper and lower 5 pixel values. Using inter-pixel values obtained from upper and lower 5 pixel values, it makes more accurate a direction estimate by applying the correlation between upper and lower pixel. If an edge direction is determined from the correlation, a missing pixel value is calculated into the average of upper and lower pixel obtained from predicted direction of edge. From simulation results, it is shown that the proposed method improves subjective image quality at edge region and objective image quality at 0.2~0.3dB as quantitative calculation result of PSNR, compared to previous various de-interlacing methods.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.543-550
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• 2000

An adaptive bit-reduced mean absolute difference (ABRMAD) is presented as a criterion for the block-matching algorithm (BMA) to reduce the complexity of the VLSI Implementation and to improve the processing time. The ABRMAD uses the lower pixel resolution of the significant bits instead of full resolution pixel values to estimate the motion vector (MV) by examining the pixels Ina block. Simulation results show that the 4-bit ABRMAD has competitive mean square error (MSE)results and a half less hardware complexity than the MAD criterion, It has also better characteristics in terms of both MSE performance and hardware complexity than the Minimax criterion and has better MSE performance than the difference pixel counting(DPC), binary block-matching with edge-map(BBME), and bit-plane matching(BPM) with the same number of bits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5481-5495
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• 2018

Single pixel imaging technology has developed for years, however the video acquisition on the single pixel camera is not a well-studied problem in computer vision. This work proposes a new scheme for single pixel camera to acquire video data and a new regularization for robust signal recovery algorithm. The method establishes a single pixel video compressive sensing scheme to reconstruct the video clips in spatial domain by recovering the difference of the consecutive frames. Different from traditional data acquisition method works in transform domain, the proposed scheme reconstructs the video frames directly in spatial domain. At the same time, a new regularization called spatial cluster is introduced to improve the performance of signal reconstruction. The regularization derives from the observation that the nonzero coefficients often tend to be clustered in the difference of the consecutive video frames. We implement an experiment platform to illustrate the effectiveness of the proposed algorithm. Numerous experiments show the well performance of video acquisition and frame reconstruction on single pixel camera.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.123-127
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• 2020

In this paper, the estimation of the disparity for depth extraction in monochrome complementary metal-oxide-semiconductor (CMOS) image sensors with offset pixel apertures is presented. To obtain the depth information, the disparity information between two different channel data of the offset pixel apertures is required. The disparity is caused by the difference in the response angle between the left- and right-offset pixel aperture images. A depth map is implemented by the generated disparity. Therefore, the disparity is the most important factor for realizing 3D images from the designed CMOS image sensor with offset pixel apertures. The disparity is influenced by the pixel height and offset value of the offset pixel aperture. To confirm this correlation, the offset value is set to maximum within the pixel area, and the disparity values corresponding to the difference in the heights are calculated and compared. The disparity is derived using the camera-lens formula. Two monochrome CMOS image sensors with offset pixel apertures are used in the disparity estimation.