Linear demosaicing inspired by the human visual system

By D. Alleysson, Sabine Süsstrunk, J. Hérault. IEEE Transactions on Image Processing, 2005

367 citations recorded by the source corpus.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/80927be3d5a08a4bc2a7a7623c3dfb2313472095

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In Atomic and Molecular Physics, and Optics

In Cognitive Neuroscience

Lei Zhang

In Computer Vision and Pattern Recognition

Citations in the corpus

Color demosaicking by local directional interpolation and nonlocal adaptive thresholding (2011, background): “Various CDM algorithms [3-18] have been proposed in the past decades.”
Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum (2010, methodology): “Before the submicrometer image sensor is envisaged, the several advanced methods [8], [9] have been proposed for the demosaicing problem (the inverse problem of reconstructing a spatially under-sampled set whose components correspond Fig.”