In single-sensor digital imaging a color filter array, that is overlaid onto the image sensor, makes color images possible. Incident light rays become band-limited and each sensor element captures either red, green or blue light. Interpolating the missing two color components for each pixel location is known as demosaicing. This paper proposes to firstly derive an estimated luminance image by low-pass filtering the original mosaiced sensor image. In a second step a deconvolution technique re-sharpenes the blurred luminance approximation, so that it has the same spatial resolution as the original - but bandpassed - sensor image. Using the high-resolution luminance approximation the partial RGB colors from the mosaiced sensor image are transformed into a different color space that is more suitable for color interpolation. The new color space consists of least correlated color data, so that intra-channel interpolation errors have a reduced impact on inter-channel alignment, and therefore result into less prominent interpolation artifacts. Demosaicing is performed on the transformed color data separately for each plane, whereby again the luminance approximation, which encodes the aligned gradient direction of all color channels, regularizes the bilinear interpolation. Finally, the result is remapped into the RGB color space to obtain the demosaiced color image. Additionally, correlated multi-channel anisotropic diffusion is applied onto the demosaiced color image to further reduce interpolation artifacts and enable denoising. The proposed algorithm is evaluated and it is concluded that - although the image formation model could be verified - its performance heavily depends on the quality of the luminance approximation, i.e. the deconvolution method.
['Johannes Herwig', 'Josef Pauli']
Regularized Color Demosaicing via Luminance Approximation