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Home / Continuation: Deep Neural Network Search for Better CFA and Demosaicing Algorithm

Continuation: Deep Neural Network Search for Better CFA and Demosaicing Algorithm

Thanks to Offline Dreams mentioned another machine learning CFA pattern optimization in the comment to my yesterday's post. "Deep Joint Design of Color Filter Arrays and Demosaicing" paper by Bernardo Henz, Eduardo S. L. Gastal, and Manuel M. Oliveira from Brazilian Instituto de Informática – UFRGS differs from the previous post in a number of ways:
  • both noisy and noiseless cases are explored
  • CFA pattern is optimized together with demosaicing algorithm
  • different CFA colors were a part of optimization too

"We present a convolutional neural network architecture for performing joint design of color filter array (CFA) patterns and demosaicing. Our generic model allows the training of CFAs of arbitrary sizes, optimizing each color filter over the entire RGB color space. The patterns and algorithms produced by our method provide high-quality color reconstructions. We demonstrate the effectiveness of our approach by showing that its results achieve higher PSNR than the ones obtained with state-of-the-art techniques on all standard demosaicing datasets, both for noise-free and noisy scenarios. Our method can also be used to obtain demosaicing strategies for pre-defined CFAs, such as the Bayer pattern, for which our results also surpass even the demosaicing algorithms specifically designed for such a pattern."


The machine learning optimization picked quite different patterns from the Bayer CFA, both in color and in size:

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