Demosaicking Scheme Research Articles

Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma Demultiplexing and Noise Level Estimation

This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.

Hybrid demosaicking scheme for artifact suppression around edges

Digital cameras use a single electronic sensor overlaid with a color filter array (CFA) to capture images for reducing the production cost. Only one primary color is sampled in each pixel such that the missing color primaries must be reconstructed by interpolation. In this paper, a hybrid demosaicking scheme for CFA interpolation is proposed to increase the visual quality of the restored image by artifact suppression in the edge region. A preliminary interpolation is first implemented by bilinear interpolation. The color difference model is then used to update green channel. The red and blue channels are successively updated using the proposed hue model that can reduce the distortion around edges. Finally, an iteratively adaptive algorithm using an edge-sensing interpolation mechanism and a luminance-color difference model is exploited to improve visual quality of the restored image by artifact suppression in the edge region. Simulation results demonstrate that the performance of the proposed algorithm is better than that of the existing approaches in terms of peak signal-to-noise ratio (PSNR) and visual quality.

Spatial-Temporal Color Video Reconstruction From Noisy CFA Sequence

Single-sensor digital video cameras use a color filter array (CFA) to capture video and a color demosaicking (CDM) procedure to reproduce the full color sequence. The reproduced video frames suffer from the inevitable sensor noise introduced in the video acquisition process. This paper presents a spatial-temporal denoising and demosaicking scheme that works without explicit motion estimation. We first perform patch based denoising on the mosaic CFA video. For each CFA patch to be denoised, similar patches are selected within a local spatial-temporal neighborhood. The principal component analysis is performed on the selected patches to remove noise. We then apply an initial single-frame CDM to the denoised CFA data, and subsequently post-process the demosaicked frames by exploiting the spatial-temporal redundancy to reduce the color artifacts. The experimental results on simulated and real noisy CFA sequences demonstrate that the proposed spatial-temporal CFA video denoising and demosaicking scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures.

Wavelet-domain demosaicking using linear estimation of interchannel correlation

The light information obtained through a color filter array is sampled as R (red), G (green), and B (blue) based on the intensity of the radiation sensor CCD charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS). Because one pixel in the CCD or CMOS has a wave value for only one color, the information on the other two colors has to be estimated from the samples of the neighboring pixels. This estimation process is called demosaicking, and it plays an important role in the image pipeline. We propose a demosaicking method that uses the correlation among the wavelet coefficients in the high-frequency region of the color channels (R, G, and B). In our method, the high-frequency wavelet coefficients are obtained by linear estimation. The low-frequency coefficients are replaced by the results obtained from bilinear interpolation. We simulate our method and compare it with existing demosaicking schemes. The experimental results show that the proposed method can perform good demosaicking.

Fast video demosaicking solution for mobile phone imaging applications

This paper introduces a cost-effective video demosaicking solution suitable for mobile phones with video capturing capabilities. The proposed solution is a spatio-temporal demosaicking scheme, which operates over three subsequent frames of the acquired video sequence. To rapidly demosaick the raw, color filter array (CFA), spatiotemporal video data our solution uses multi-stage processing concepts and builds on the principles of refined spectral modeling and self-adaptation to varying spatiotemporal characteristics. The proposed solution accommodates cost-effective considerations inherent in mobile phone imaging and produces a visually pleasing demosaicked video sequence.