• JSTS:Journal of Semiconductor Technology and Science
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• 제6권4호
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• pp.270-274
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• 2006

A color filter array (CFA) helps a single electrical image sensor to recognize color images. The Red-Green-Blue (RGB) Bayer CFA is commonly used, but the amount of the light which arrives at the photodiode is attenuated with this CFA. Red-White-Blue (RWB) CFA increases the amount of the light which arrives at photodiode by using White (W) pixels instead of Green (G) pixels. However, white pixels are saturated earlier than red and blue pixels. The pseudo multiple capture scheme and the corresponding RWB CFA were proposed to overcome the early saturation problem of W pixels. The prototype CMOS image sensor (CIS) was fabricated with $0.35-{\mu}m$ CMOS process. The proposed CIS solves the early saturation problem of W pixels and increases the dynamic range.

• IEIE Transactions on Smart Processing and Computing
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• 제3권2호
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• pp.52-58
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• 2014

Exact histogram specification (EHS) transforms the histogram of an input image into the specified histogram. In the conventional EHS techniques, the pixels are first sorted according to their graylevels, and the pixels that have the same graylevel are further differentiated according to the local average of the pixel values and the edge strength. The strictly ordered pixels are then mapped to the desired histogram. However, since the conventional sorting method is inherently dependent on the initial graylevel-based sorting, the contrast enhancement capability of the conventional EHS algorithms is restricted. We propose a modified EHS algorithm considering the just noticeable difference. In the proposed algorithm, the edge pixels are pre-processed such that the output edge pixels obtained by the modified EHS can result in the local contrast enhancement. Moreover, we introduce a new sorting method for the pixels that have the same graylevel. Experimental results show that the proposed algorithm provides better image enhancement performance compared to the conventional EHS algorithms.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권6호
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• pp.1718-1734
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• 2012

The proposed method in this paper is derived from Mielikainen's hiding method. However, there exist some significant differences between two methods. In Mielikainen's method, pixels are partitioned into pairs and a LSB matching function is applied to two pixels for hiding. On the contrary, the proposed method partitions pixels into groups with three pixels in each group. The bits of pixels in each group are linked by using an exclusive OR (XOR) operator in a circular way. If the number of different values between the calculated XOR values and the secret bits is smaller than or equal to 2 in a group, the proposed method can guarantee that at most one pixel is needed to be modified by adding/subtracting its value to/from one, and three secret bits can be embedded to three pixels. Through theoretical analysis, the amount of the embedded secret data in the proposed method is larger than those in other methods under the same amount of pixel modifications. Taking real images in our experiments, the quality of stego-images in the proposed method is higher than those in other methods.

• 한국기계가공학회지
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• 제8권2호
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• pp.25-31
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• 2009

The image processing methods are widely used in many industrial fields to detect defections in inspection devices. In this study an image processing method was conducted for the detection of abnormal pixels in a OLED(Organic Light Emitting Diode) type panel which is used for small size displays. The display quality of an OLED device is dependent on the pixel formation quality. So, among the so many pixels, to find out the faulty pixels is very important task in manufacturing processing or inspection division. We used a line scanning type BW(Black & White) camera which has very high resolution characteristics to acquire an image of display pixel patterns. And the various faulty cases in pixel abnormal patterns are considered to detect abnormal pixels. From the results of the research, the normal BW pixel image could be restored to its original color pixel.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 추계학술대회
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• pp.204-208
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• 2011

This paper proposes a novel deinterlacing algorithm using neighboring deinterlacing pixels directions weight, which can obtain the true deinterlacing direction of the interpolated pixel. The proposed algorithm determines the direction of the current interpolated pixel using MELA direction determination method. To obtain more accurate deinterlacing direction and increase interpolation direction correlation, the directions of neighboring pixels around the current interpolated pixel are considered. The current direction and the neighboring majority direction are compared to decide an interpolated method. But it cost slightly CPU time increasing since neighboring pixels directions determination and statistics. Experimental results demonstrate that the proposed algorithm outperforms the conventional deinterlacing methods.

• 한국멀티미디어학회논문지
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• 제18권12호
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• pp.1519-1527
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• 2015

TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2928-2947
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• 2015

Adaptive Gaussian filter (AGF) is a recently developed switching filter to remove salt and pepper noise. AGF first directly identifies pixels of gray levels 0 and 255 as noise pixels, and then only restored noise pixels using a Gaussian filter with adaptive variance based on the estimated noise density. AGF usually achieves better denoising effect in comparison with other filters. However, AGF still fails to obtain good denoising effect on images with noise-free pixels of gray levels 0 and 255, due to its severe false alarm in its noise detection stage. To alleviate this issue, a modified version of AGF is proposed in this paper. Specifically, the proposed filter first performs noise detection via an image block based noise density estimation and sequential noise density guided rectification on the noise detection result of AGF. Then, a modified Gaussian filter with adaptive variance and window size is used to restore the detected noise pixels. The proposed filter has been extensively evaluated on two representative grayscale images and the Berkeley image dataset BSDS300 with 300 images. Experimental results showed that the proposed filter achieved better denoising effect over the state-of-the-art filters, especially on images with noise-free pixels of gray levels 0 and 255.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3873-3887
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• 2018

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.