Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH Asia 2008 papers
Direct super-resolution and registration using raw CFA images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Primary-consistent soft-decision color demosaicking for digital cameras (patent pending)
IEEE Transactions on Image Processing
Adaptive homogeneity-directed demosaicing algorithm
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation
IEEE Transactions on Image Processing
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Upsampling with preserving image details is highly demanded image operation. There are various upsampling algorithms. Many upsampling algorithms focus on the gray image. For color images, those algorithms are usually applied to a luminance component only, or independently applied channel by channel. However, we can not observe the full-color image by a single image sensor equipped in a common digital camera. The data observed by the single image sensor is called raw data. The raw data is converted into the full-color image by demosaicing. Upsampling from the raw data requires sequential processes of demosaicing and upsampling. In this paper, we propose direct upsampling from the raw data based on a kernel regression. Although the kernel regression is known as powerful denoising and interpolation algorithm, the kernel regression has been also proposed for the gray image. We extend to the color kernel regression which can generate the full-color image from any kind of raw data. Second key point of the proposed color kernel regression is a local density parameter optimization, or kernel size optimization, based on the stability of the linear system associated to the kernel regression. We also propose a novel iteration framework for the upsampling. The experimental results demonstrate that the proposed color kernel regression outperforms existing sequential approaches, reconstruction approaches, and existing kernel regression.