Olivier Losson

Comparison of color demosaicing methods

Mono-CCD color cameras acquire only one color component at each pixel by means of their color filter array (CFA) covering the CCD sensor. To obtain a color image, a procedure - called demosaicing - is then necessary to estimate the other two missing color components at each pixel. This chapter deals with the quality of color images generated in such a way. We attempt to determine which demosaicing method provides the best results according to several comparison criteria, particularly for subsequent needs of color image analyses like edge detection.

Quality Evaluation of Color Demosaicing According to Image Resolution

To obtain a true color image from the Bayer CFA samples, an estimation process using interpolation is performed to retrieve the missing color components of each pixel. This is commonly referred to as CFA demosaicing. In this paper, we study the relationship between the resolution of the acquired CFA image and the quality of the demosaiced color image. For this purpose, we propose new criteria based especially on the detected edges of the objects present in the scene.

Color local binary patterns: compact descriptors for texture classification

Abstract. Texture description is a challenging problem with color images. Despite some attempts to include colors in local binary patterns (LBPs), no proposal has emerged as a color counterpart of grayscale LBPs. This is because colors are defined by vectors that are not naturally ordered and several ways exist to compare them. We propose an LBP extension that takes the vector information of color into account due to a color order. As several color orders are available and the selection of the most suitable one is difficult, we combine two of them in a texture descriptor called “mixed color order LBPs.” This small-size feature provides good performance on several benchmark databases for two classification problems with regard to larger-size LBP-based features of color textures.

Multispectral demosaicing using intensity-based spectral correlation

Single-sensor color cameras, which classically use a color filter array (CFA) to sample RGB channels, have recently been extended to the multispectral domain. To sample more than three wavelength bands, such systems use a multispectral filter array (MSFA) that provides a raw image in which a single channel level is available at each pixel. A demosaicing procedure is then needed to estimate a multispectral image with full spectral resolution. In this paper, we propose a new demosaicing method that takes spectral and spatial correlations into account by estimating the level for each channel. Experimental results show that it provides estimated images of better quality than classical methods.

Multispectral Demosaicing Using Pseudo-Panchromatic Image

Single-sensor color cameras, which classically use a color filter array to sample RGB channels, have recently been extended to the multispectral domain. To sample more than three wavelength bands, such systems use a multispectral filter array that provides a raw image in which a single channel value is available at each pixel. A demosaicing procedure is then needed to estimate a fully defined multispectral image. In this paper, we review multispectral demosaicing methods and propose a new one based on the pseudo-panchromatic image (PPI). Pixel values in the PPI are computed as the average spectral values. Experimental results show that our method provides estimated images of better quality than classical ones.

Color Image Segmentation by Compacigram Analysis

This work lies within the scope of color image segmentation by pixel classification. Classes of pixels are generally constructed by mode detection applied to the color histogram, but this method tends to fail when the color distributions of the different objects highly overlap in the color space. A new characteristic function of the distribution of the colors within the image is proposed here. The compacigram takes into account both the distribution of colors in the color space and the spatial location of colors in the image plane. Experimental tests on two reference images show that the mode detection technique based on a convexity analysis of the compacigram leads to promising results for segmentation purposes.

CFA local binary patterns for fast illuminant-invariant color texture classification

This paper focuses on the classification of color textures acquired by single-sensor color cameras under various illuminants. Local binary patterns (LBPs) are robust texture descriptors suited to such conditions. This property is still improved when LBPs are computed from the level ranks. Our main contribution is to avoid the demosaicing step that is classically performed in single-sensor color cameras to estimate color images from raw data. We instead compute rank-based LBPs from the color filter array image, in which each pixel is associated to a single color component. Experimental results achieved on a benchmark color texture database show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency.

Fuzzy mode enhancement and detection for color image segmentation

This work lies within the scope of color image segmentation by pixel classification. The classes of pixels are constructed by detecting the modes of the spatial-color compactness function, which characterizes the image by taking into account both the distribution of colors in the color space and their spatial location in the image plane. A fuzzy transformation of this function is performed, based on fuzzy morphological operators specifically designed for mode detection. Experimental segmentation results, using several synthetic and benchmark images, show the interest of the proposed method.

Multispectral Demosaicing Using Intensity in Edge-Sensing and Iterative Difference-Based Methods

Single-sensor multispectral cameras, that sample spectral channels using a multispectral filter array, have recently emerged. They provide a raw image in which each channel is spectrally sampled pixel-wise according to the filter array pattern. A demosaicing procedure is then needed to estimate a multispectral image with full spectral resolution. The usefulness of intensity-based demosaicing has been shown in a previous work. In this paper, we both propose an intensity-based adaptation of the Binary Tree-based Edge-Sensing method for raw images with no dominant spectral band, and a new iterative method that uses the central wavelength distance between spectral bands.

Texture classification with fuzzy color co-occurrence matrices

Chromatic co-occurrence matrices (Chromatic CMs) are well-known and efficient texture descriptors and Fuzzy CMs (FCMs) have been designed to characterize grey-level texture images. In this paper, we propose to extend FCMs to color images and to apply them for efficient texture classification.