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Computationally efficient locally adaptive demosaicing of color filter array images using the dual-tree complex wavelet packet transform.

Aelterman J, Goossens B, De Vylder J, Pi┼żurica A, Philips W - PLoS ONE (2013)

Bottom Line: By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way.Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost.The wavelet approach also allows for computationally effective denoising or deblurring approaches.

View Article: PubMed Central - PubMed

Affiliation: IPI-TELIN-IMINDS, Ghent University, Ghent, Belgium. jaelterm@telin.ugent.be

ABSTRACT
Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way. Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost. The wavelet approach also allows for computationally effective denoising or deblurring approaches.

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Power spectra of the filters from a bilinear demosaicing filter implementation (black means high power spectral density).
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pone-0061846-g003: Power spectra of the filters from a bilinear demosaicing filter implementation (black means high power spectral density).

Mentions: The goal of demosaicing is to reverse the mosaicing operation implemented by the CFA. The most straightforward (linear) demosaicing algorithms demultiplex and filter the different color channels in pixel domain, resulting in a low-pass filtered result. For bilinear interpolation, the corresponding low-pass filters are shown in Figure 3. Note the lower bandwidth for the red/blue filter (right) than for the green filter (left). Also note how the mosaic-related aliases (bottom row of Figure 2) are nicely suppressed by the low-pass filters. The aliasing, related to overlapping power spectra problem has been largely avoided because these low-pass filters have a fairly low bandwidth and essentially serve as aliasing suppression filters. The low bandwidth is a disadvantage, as it reduces image sharpness. The most important way in which more advanced demosaicing techniques, such as the ones mentioned in the introduction, distinguish themselves is by increasing reconstruction bandwidth, while avoiding aliasing/demosaicing artifacts by (simple) non-linear operations. This is also the case for the method presented in this paper. Note that roughly, the bandwidth for green is 75% of the total bandwidth, while the bandwidth for red and blue signals in the reconstructed image is 25% of the total bandwidth.


Computationally efficient locally adaptive demosaicing of color filter array images using the dual-tree complex wavelet packet transform.

Aelterman J, Goossens B, De Vylder J, Pi┼żurica A, Philips W - PLoS ONE (2013)

Power spectra of the filters from a bilinear demosaicing filter implementation (black means high power spectral density).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643977&req=5

pone-0061846-g003: Power spectra of the filters from a bilinear demosaicing filter implementation (black means high power spectral density).
Mentions: The goal of demosaicing is to reverse the mosaicing operation implemented by the CFA. The most straightforward (linear) demosaicing algorithms demultiplex and filter the different color channels in pixel domain, resulting in a low-pass filtered result. For bilinear interpolation, the corresponding low-pass filters are shown in Figure 3. Note the lower bandwidth for the red/blue filter (right) than for the green filter (left). Also note how the mosaic-related aliases (bottom row of Figure 2) are nicely suppressed by the low-pass filters. The aliasing, related to overlapping power spectra problem has been largely avoided because these low-pass filters have a fairly low bandwidth and essentially serve as aliasing suppression filters. The low bandwidth is a disadvantage, as it reduces image sharpness. The most important way in which more advanced demosaicing techniques, such as the ones mentioned in the introduction, distinguish themselves is by increasing reconstruction bandwidth, while avoiding aliasing/demosaicing artifacts by (simple) non-linear operations. This is also the case for the method presented in this paper. Note that roughly, the bandwidth for green is 75% of the total bandwidth, while the bandwidth for red and blue signals in the reconstructed image is 25% of the total bandwidth.

Bottom Line: By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way.Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost.The wavelet approach also allows for computationally effective denoising or deblurring approaches.

View Article: PubMed Central - PubMed

Affiliation: IPI-TELIN-IMINDS, Ghent University, Ghent, Belgium. jaelterm@telin.ugent.be

ABSTRACT
Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way. Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost. The wavelet approach also allows for computationally effective denoising or deblurring approaches.

Show MeSH