Single sensor digital cameras use color filter arrays to capture a subset of the color data at each pixel coordinate. Demosaicing or color filter array (CFA) interpolation is the process of estimating the missing color samples to reconstruct a full color image. In this paper, we propose a demosaicing method that uses multiscale color gradients to adaptively combine color difference estimates from different directions. The proposed solution does not require any thresholds since it does not make any hard decisions, and it is noniterative. Although most suitable for the Bayer CFA pattern, the method can be extended to other mosaic patterns. To demonstrate this, we describe its application to the Lukac CFA pattern. Experimental results show that it outperforms other available demosaicing methods by a clear margin in terms of CPSNR and S-CIELAB measures for both mosaic patterns.

In this paper, we describe an embedded programmable stereoscopic 3D camera system for mobile platforms. With the proliferation of stereoscopic televisions, computer monitors, and even auto-stereoscopic LCDs, as well as a growing inventory of stereoscopic games and movies, there is a growing demand for stereoscopic 3D content creation. We present each of the key components that enable the stereoscopic 3D video capture and playback on the embedded platform, including imaging, codec, graphics, and display subsystems. We describe several unique and new 3D image processing algorithms and explain how they are integrated into the stereo 3D system.

For economic reasons, most digital cameras use color filter arrays instead of beam splitters to capture image data. As a result of this, only one of the required three color samples becomes available at each pixel location and the other two need to be interpolated. This process is called Color Filter Array (CFA) interpolation or demosaicing. Many demosaicing algorithms have been introduced over the years to improve subjective and objective interpolation quality. We propose an orientation-free edge strength filter and apply it to the demosaicing problem. Edge strength filter output is utilized both to improve the initial green channel interpolation and to apply the constant color difference rule adaptively. This simple edge directed method yields visually pleasing results with high CPSNR.

Most of the current digital cameras feature a single sensor de sign which limits the number of channels recorded at each pixel location to one. However, a color image is represented with three channels for each pixel. Color Filter Array (CFA) interpolation is the process of generating a full three channel color image from a single channel mosaicked input. We pro pose a simple edge strength filter to interpolate the missing color values adaptively. While the filter is readily applicable to the Bayer mosaic pattern, we argue that the same idea could be extended to other mosaic patterns and describe its application to the Lukac mosaic pattern. The proposed solution outperforms other available algorithms for the Lukac pattern in terms of both objective and subjective comparison.

Single sensor digital cameras capture one color value for every pixel location. The remaining two color channel values need to be estimated to obtain a complete color image. This process is called demosaicing or Color Filter Array (CFA) interpolation. We propose a directional approach to the CFA interpolation problem that makes use of multiscale color gradients. The relationship between color gradients on different scales is used to generate signals in vertical and horizontal directions. We determine how much each direction should contribute to the green channel interpolation based on these signals. The proposed method is easy to implement since it is noniterative and threshold free. Experiments on test images show that it offers superior objective and subjective interpolation quality.

Color Filter Array (CFA) interpolation is an integral part of image processing pipeline for single sensor digital cameras. Many CFA algorithms have been proposed over the years to improve resulting image quality. One such algorithm is the highly successful Directional Linear Minimum Mean-Square Error Estimation (DLMMSE) method. We make several observations on this algorithm and propose a new method to address those points. The proposed method yields visually pleasing results and outperforms all CFA interpolation algorithms that are included in a recent survey paper in terms of PSNR.