Perceptual assessment of demosaicing algorithm performance

Longère, Philippe; Zhang, Xuemei; Delahunt, Peter B.; Brainard, David H.

doi:10.1109/5.982410

Cited by 122 publications

(110 citation statements)

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“…To facilitate the comparisons, the images have been normalized to a standard size of 512 × 512 pixels with a 8-bits per channel RGB representation. Since the original Bayer image is usually unavailable in order to obtain test Bayer image data used in CFA interpolation researchers sample original images with the Bayer CFA pattern [10], [11]. The restored outputs are obtained using demosaicking (CFA interpolation) solutions.…”

Section: Resultsmentioning

confidence: 99%

Bayer Pattern Demosaicking Using Local-Correlation Approach

Lukáč

Plataniotis

Venetsanopoulos

2004

Computational Science - ICCS 2004

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Abstract.A new Bayer pattern demosaicking scheme for single-sensor digital cameras is introduced. The raw output from a sensor, mostly a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) sensor, with a Bayer filter represents a mosaic of red, green and blue pixels of different intensity. To interpolate the two missing color components in each spatial location and constitute the full color, camera output, the proposed method utilizes edge-sensing interpolation and correction steps. Since the correction step is suitable only for the image regions with high spectral correlation, otherwise is counter productive, the scheme is adaptively controlled through the comparisons between the correlation coefficient and the pre-determined parameter. The proposed method yields excellent performance, in terms of subjective and objective image quality measures, and outperforms previously developed CFA interpolation solutions.

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Section: Resultsmentioning

confidence: 99%

Bayer Pattern Demosaicking Using Local-Correlation Approach

Lukáč

Plataniotis

Venetsanopoulos

2004

Computational Science - ICCS 2004

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“…The zooming can be achieved by applying either plane-wise interpolation [8] or vector color interpolation [9] on the demosaiced image. However, demosaicing always introduces visual artifacts such as blurred edges and false colors [10]. These artifacts may be amplified during the zooming process and the quality of the resultant zoomed image can be very poor in regions of complicated details.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Combined Demosaicing and Zooming Algorithm for Digital Camera

Chung

Chan

et al. 2007

2007 IEEE International Conference on Image Processing

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Abstract-Color demosaicing and digital zooming are common processes in digital cameras and they often employ similar interpolation concepts based on the information extracted from the raw sensor data. Realizing them independently is not efficient as separate extraction processes are required. It may also cause inconsistent utilization of the raw sensor data in different stages. This paper presents a low-complexity combined algorithm which directly extracts edge information from raw sensor data and exploits it consistently and efficiently in both demosaicing and zooming. The proposed algorithm can produce zoomed full-color images and zoomed CFA images with outstanding performance as compared with conventional approaches.

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“…The full color image is reconstructed by interpolating the missing color samples. Color demosaicking has been extensively studied in spatial domain for still digital cameras [1], [2], [5]- [13], [15]- [21], [24], [25], [27], [28]. We call this class of color demosaicking techniques the intra-frame or spatial demosaicking.…”

mentioning

confidence: 99%

“…The earlier spatial demosaicking methods, such as nearest-neighbor replication and bilinear and bicubic interpolation [15], can be simply implemented, but they suffer from many artifacts such as blocking, blurring, and zipper effect at edges. Lately developed demosaicking methods exploited the correlation between color channels.…”

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confidence: 99%

Temporal color video demosaicking via motion estimation and data fusion

Zhang

2006

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Color demosaicking of charge-coupled device (CCD) data has been thoroughly studied for single-sensor still digital cameras. However, there has seemingly been little research on color demosaicking techniques for single-sensor video digital cameras. The temporal dimension of a color mosaic image sequence can reveal new information on the missing color components due to the mosaic subsampling, which is otherwise unavailable in the spatial domain of individual frames. This paper proposes a temporal approach to color demosaicking. A pixel of the current frame is matched to another in a reference frame via motion analysis, such that the CCD sensor samples different color components of the same object position in the two frames. The resulting inter-frame estimates of missing color components are fused with suitable intra-frame estimates to achieve a more robust color restoration. Our experimental results demonstrate clear advantages of the presented temporal color demosaicking approach over its intra-frame counterparts in reducing the color artifacts.Index Terms-Bayer pattern, color demosaicking, data fusion, motion estimation, single-sensor digital video cameras.

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Perceptual assessment of demosaicing algorithm performance

Abstract: Demosaicing is an important part of the image-processing chain

Cited by 122 publications

References 10 publications

Bayer Pattern Demosaicking Using Local-Correlation Approach

Bayer Pattern Demosaicking Using Local-Correlation Approach

An Efficient Combined Demosaicing and Zooming Algorithm for Digital Camera

Temporal color video demosaicking via motion estimation and data fusion

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