Color demosaicking via directional linear minimum mean square-error estimation

By Lei Zhang, Xiaolin Wu. IEEE Transactions on Image Processing, 2005

413 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/b2542c35c99f8bbeea12eb369027879559a8255b

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Color demosaicking by local directional interpolation and nonlocal adaptive thresholding (2011, background): “…interpolation and nonlocal adaptive thresholding Lei Zhang Xiaolin Wu Antoni Buades Xin Li Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/05/2013 Terms of Use: http://spiedl.org/terms Color demosaicking by local directional interpolation and nonlocal adaptive…”
The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in‐flight calibration, validation, and data archiving (2017, methodology): “We have also experimented with simple bilinear interpolation and Directional Linear Minimum Mean Square-Error Estimation (DLMMSE, also known as Zhang-Wu demosaicing [Zhang and Wu, 2005]) algorithms.”
Loss Functions for Neural Networks for Image Processing (2015, methodology): “Beyond considering different cost functions for training, we also compare the output of our network with the images obtained by a state-of-the-art denoising method, BM3D, operating directly in the Bayer domain [26], followed by a standard demosaicking algo-rithm [27].”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “All these methods require considerable A u t h o r ' s p e r s o n a l”
Loss Functions for Image Restoration With Neural Networks (2017, methodology): “Beyond considering different cost functions for training, we also compare the output of our network with the images obtained by a state-of-the-art denoising method, BM3D, operating directly in the Bayer domain [27], followed by a standard demosaicking algorithm [28].”