A Low-Complexity Joint Color Demosaicking and Zooming Algorithm for Digital Camera

Chung, King-Hong; Chan, Yuk‐Hee

doi:10.1109/tip.2007.898997

Cited by 23 publications

(4 citation statements)

References 25 publications

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“…In the proposed demosaicing method, the high performance two-step estimation scheme proposed in Ref. 26 is modified in three aspects to estimate the missing green samples at a reduced complexity. First, instead of the parameters L H and L V used in Ref.…”

Section: Green Plane Interpolationmentioning

confidence: 99%

“…First, instead of the parameters L H and L V used in Ref. 26, IGs are utilized to determine the interpolation direction of the missing green sample to improve the performance. Second, pixels that obviously should not be interpolated horizontally or vertically are processed in the first pass rather than in the second pass to save computation effort.…”

Section: Green Plane Interpolationmentioning

confidence: 99%

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Low-complexity color demosaicing algorithm based on integrated gradients

Chung

Chan

2010

J. Electron. Imaging

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Color demosaicing is critical for digital cameras, because it converts a Bayer sensor mosaic output to a full color image, which determines the output image quality of the camera. In this work, an efficient decision-based demosaicing method is presented. This method exploits a new edge-sensing measure called integrated gradient (IG) to effectively extract gradient information in both color intensity and color difference domains simultaneously. This measure is reliable and supports full resolution, which allows one to interpolate the missing samples along an appropriate direction and hence directly improves the demosaicing performance. By sharing it in different demosaicing stages to guide the interpolation of various color planes, it guarantees the consistency of the interpolation direction in different color channels and saves the effort required to repeatedly extract gradient information from intermediate interpolation results at different stages. An IG-based green plane enhancement is also proposed to further improve the method's efficiency. Simulation results confirm that the proposed demosaicing method outperforms up-to-date demosaicing methods in terms of output quality at a complexity of around 80 arithmetic operations per pixel.

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Section: Green Plane Interpolationmentioning

confidence: 99%

Section: Green Plane Interpolationmentioning

confidence: 99%

Low-complexity color demosaicing algorithm based on integrated gradients

Chung

Chan

2010

J. Electron. Imaging

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“…Execution times have also been used in [32,33] for evaluating the computational cost of algorithms. The comparison tests are performed for several transformations on computers with known characteristics.…”

Section: Runtime Comparisonsmentioning

confidence: 99%

A survey on evaluation methods for image interpolation

Amanatiadis

Andreadis

2009

Meas. Sci. Technol.

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Image interpolation is applied to Euclidean, affine and projective transformations in numerous imaging applications. However, due to the unique characteristics and wide applications of image interpolation, a separate study of their evaluation methods is crucial. The paper studies different existing methods for the evaluation of image interpolation techniques. Furthermore, an evaluation method utilizing ground truth images for the comparisons is proposed. Two main classes of analysis are proposed as the basis for the assessments: performance evaluation and cost evaluation. The presented methods are briefly described, followed by comparative discussions. This survey provides information for the appropriate use of the existing evaluation methods and their improvement, assisting also in the designing of new evaluation methods and techniques.

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“…On the other hand, L. Zhang and D. Zhang proposed in [19] a joint demosaicking-zoomingalgorithm based on the computation of the color difference signals using the high spectral-spatial correlations in the CFA image to suppress artefacts arising from demosaicking as well as zippers and rings arising from zooming. Likewise, Chung and Chan proposed in [20] a joint demosaicking-zoomingalgorithm based on the interpolation of edge information extracted from raw sensor data in order to preserve edge features in output image. Lastly, Wu and Zhang proposed in [21,22] a temporal color video demosaicking algorithm based on the motion estimation and data fusion in order to reduce color artefacts over the intraframes.…”

Section: Color Acquisition Systemsmentioning

confidence: 99%

Color in Image and Video Processing: Most Recent Trends and Future Research Directions

Trémeau¹,

Tominaga²,

Plataniotis³

2008

EURASIP Journal on Image and Video Processing

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The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

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A Low-Complexity Joint Color Demosaicking and Zooming Algorithm for Digital Camera

Cited by 23 publications

References 25 publications

Low-complexity color demosaicing algorithm based on integrated gradients

Low-complexity color demosaicing algorithm based on integrated gradients

A survey on evaluation methods for image interpolation

Color in Image and Video Processing: Most Recent Trends and Future Research Directions

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