• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.242-245
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• 2000

In this paper, we present a color interpolation technique based on artificial neural networks for a single-chip CCD (charge-coupled device) camera with a Bayer color filter array (CFA). Single-chip digital cameras use a color filter array and an interpolation method in order to regenerate high quality color images from sparsely sampled images. We applied 3-layer feedforward neural networks in order to interpolate missing pixel from surrounding pixels. And we compared the proposed method with conventional interpolation methods such as the proposed interpolation algorithm based on neural networks provides a better performance than the conventional interpolation algorithms.

• ETRI Journal
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• 제38권1호
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• pp.164-173
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• 2016

Demosaicing, or color filter array (CFA) interpolation, estimates missing color channels of raw mosaiced images from a CFA to reproduce full-color images. It is an essential process for single-sensor digital cameras with CFAs. In this paper, a new demosaicing method for digital cameras with Bayer-like W-RGB CFAs is proposed. To preserve the edge structure when reproducing full-color images, we propose an edge direction-adaptive method using color difference estimation between different channels, which can be applied to practical digital camera use. To evaluate the performance of the proposed method in terms of CPSNR, FSIM, and S-CIELAB color distance measures, we perform simulations on sets of mosaiced images captured by an actual prototype digital camera with a Bayer-like W-RGB CFA. The simulation results show that the proposed method demosaics better than a conventional one by approximately +22.4% CPSNR, +0.9% FSIM, and +36.7% S-CIELAB distance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.171-173
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• 2000

In this paper, we present a new interpolation method for the color filter away(CFA). In order to capture color images. typical input devices use a single chip CCD imaging sensor with color filter array. As a result, the single chip CCD does not provide sufficient color resolutions since it arranges different color filters sequentially on a single CCD, resulting in aliasing noise and loss of resolution. In order to reconstruct high quality color images, we propose to use the interpolation algorithm using high order B-splines. Experiments show promising results.

• 한국정보통신학회논문지
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• 제14권7호
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• pp.1581-1588
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• 2010

대부분의 디지털 촬영 장치는 비용 및 속도 상의 이점을 위해 Color Filter Array(CFA)를 포함하고 있는 단일 이미지 센서를 사용한다. 따라서 완전한 컬러 영상으로 복원하기 위하여 다양한 컬러 보간 방법이 개발되고 있다. 본 논문은 이러한 컬러 보간 방법 중, 방향성 필터를 이용하여 수직, 수평, 대각선 방향의 에지를 고려한 컬러 보간 방법에 관한 것이다. 하드웨어 구현을 위해 하드웨어 자원의 효율성을 고려하였다. Kodak 테스트 영상 24장으로 실험하여 기존 방법과 비교함으로써 제안한 방법의 성능을 확인하였다. Verilog HDL로 구현하였으며, Virtex4 FPGA 보드와 CMOS 이미지 센서를 이용하여 검증하였다.

• 전기학회논문지
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• 제63권10호
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• pp.1434-1440
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• 2014

This paper describes a demosaicking method for hexagonally-structured color filter array. Demosaicking is essential to acquire color images using color filter array (CFA) in single sensor imaging. Thus, CFA patterns have been discussed in order to improve image quality in single sensor imaging after the Bayer pattern are introduced. Advancements in imaging sensor technology recently introduce a hexagonal CFA pattern. The hexagonal CFA can be considered to be a 45-degree rotational version of the Bayer pattern, thus demosaicking can be implemented by an existing method with backward and forward 45-degree rotations. However, this approach requires heavy computing power and memory in image sensing devices because of the image rotations. To overcome this problem, we proposes a demosaicking method for a hexagonal Bayer CFA without rotations. In addition, we introduce a weighting parameter in our demosaicking method to improve image quality and to unifying exiting method with our method. Experimental results indicate that the proposed method is superior to conventional methods in terms of PSNR. In addition, some optimized values for the weighting parameter are provided experimentally.

• 한국컴퓨터정보학회논문지
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• 제15권10호
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• pp.59-70
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• 2010

모바일폰이나 디지털 카메라에서 사용하는 영상 센서의 수광 소자는 한 가지 색만을 기록하는 베이어 패턴이기 때문에 각각의 화소들은 여러 개의컬러 중에서단 하나만의 컬러에 대한 화소값을얻게 된다. 잃어버린화소의 정보는 CFA(Color Filter Array)를 이용하여 그 주위의 화소에 대한 정보를 이용함으로 완전한 컬러 영상을 추론한다. 본 논문은 영상 센서의 수광 소자로부터 획득한 데이터의 평균과 각 컬러 채널 데이터 평균 사이의 관계를 유도하고, 이 관계를 이용하여 색상 보간을 위한 효율적인 에지 컬러 퍼지 보간 방법을 제안하였다. 또한 휘도 신호 채널의 보간은 컬러 노이즈를 감소하고 효율적인 보간을 위해 에지 방향에 따라 퍼지 보간을 하였다. 그리고 본 논문에서 PSNR을 이용하여 제안된 방법이 기존 방법보다 평균 2.4dB 정도 개선됨을 증명하였으며, 시각적으로 원영상의 해상도와 유사한 수준이며, 기존 알고리즘의 영상보다 색수차가 감소된 것을 실험 결과를 통하여 확인하였다.

• 방송공학회논문지
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• 제14권6호
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• pp.742-748
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• 2009

대부분의 상업적 디지털카메라들은 가격과 크기를 줄이기 위해서 Color Filter Array (CFA)를 사용한다. CFA를 통과한 이미지는 픽셀 당 오직 단일 칼라정보만 가지고 있기 때문에, 정보를 잃어버린 나머지 2개의 색 정보를 얻기 위해서 보간을 해야하는데 이 과정을 디모자이킹 (Demosaicing) 이라고 부른다. 수 많은 디모자이킹 알고리듬 방법들이 제안되어 왔음에도 불구하고, 여전히 무지개 형태의 에러 (rainbow artifact) 와 지퍼 모양의 에러 (zippering artifact) 의 문제를 가지고 있다. 본 논문에서는 향상된 가중치 함수를 이용하여 주변 픽셀값들의 경향성을 이용한 디모자이킹 알고리듬 방법을 제안한다. 실험결과, 우리가 제안한 알고리듬은 주관적 화질뿐만 아니라 객관적 화질에서도 기존의 방법들보다 뛰어다나는 것을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권3호
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• pp.1011-1025
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• 2014

A demosaicing method for a Bayer-like W-RGB color filter array (CFA) is proposed. When reproducing images from a W-RGB CFA, conventional color separation methods for W-RGB CFA are likely to cause blurring near the edges due to rough averaging using a color ratio of neighboring pixels. Moreover, these methods cannot be applied to real-life digital cameras with W-RGB CFA because the methods were proposed under an ideal situation, W=R+G+B, not a real-life situation, $W{\neq}R+G+B$. To improve edge performance, we propose a method of constant color difference assumption with inversed weight, which uses information from all edge directions for interpolating all missing color channels. The proposed method calculates the correlation between W, R, G, and B to enable its application to real-life digital cameras with W-RGB CFA. Simulations were performed to evaluate the proposed method using images captured from a real-life digital camera with W-RGB CFA. Simulation results shows that we can demosaic by using the proposed algorithm compared with the conventional one in about +34.79% SNR, +11.43% PSNR, +1.54% SSIM and 14.02% S-CIELAB error. Thus, the proposed method demosaics better than the conventional methods.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.13-21
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• 2022

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.1118-1121
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• 2010

With the advent of cell phone digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital camera equipped in cell phones employ a single image sensor in conjunction with a color filter array (CFA), and then interpolates the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms in test cell phones.