Abstract-This paper presents an adaptive demosaicing algorithm. Missing green samples are first estimated based on the variances of the color differences along different edge directions. The missing red and blue components are then estimated based on the interpolated green plane. This algorithm can effectively preserve the details in texture regions and, at the same time, it can significantly reduce the color artifacts. As compared with the latest demosaicing algorithms, the proposed algorithm produces the best average demosaicing performance both objectively and subjectively.
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.
This paper presents a low complexity joint color demosaicking and digital zooming algorithm for single-sensor digital cameras. The proposed algorithm directly extracts edge information from raw sensor data for interpolation in both demosaicking and zooming to preserve edge features in its output. This allows the extracted information to be exploited consistently in both stages and also efficiently, as no separate extraction process is required in different stages. The proposed algorithm can produce a zoomed full-color image as well as a zoomed Bayer color filter array image with outstanding performance as compared with conventional approaches which generally combine separate color demosaicking and digital zooming schemes.
Demosaicing and compression are generally performed sequentially in most digital cameras. Recent reports show that the compression-first scheme outperforms the conventional demosaicing-first scheme in terms of image quality and complexity. In this paper, an efficient lossless compression scheme for Bayer images is presented. It exploits a context matching technique to rank the neighboring pixels for predicting a pixel. Besides, an adaptive color difference estimation scheme is also proposed to remove the spectral redundancy. Simulation results show that the proposed algorithm can achieve a better compression performance as compared with the existing lossless CFA image coding methods.
In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.
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