High-quality linear interpolation for demosaicing of Bayer-patterned color images

Malvar, H.S.; He, Long; Cutler, Ross

doi:10.1109/icassp.2004.1326587

Cited by 348 publications

(297 citation statements)

References 8 publications

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“…The first step performed in DEA logic to create a JPEG from a raw image is to perform a Bayer pattern interpolation based on the method of Malvar et al (2004). The interpolation kernels have been manipulated to directly produce luminance (Y) and chrominance (C R , C B ) components.…”

Section: Lossy Compressed Datamentioning

confidence: 99%

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

et al. 2012

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The Mars Science Laboratory (MSL) Mars Hand Lens Imager (MAHLI) investigation will use a 2-megapixel color camera with a focusable macro lens aboard the rover, Curiosity, to investigate the stratigraphy and grain-scale texture, structure, mineralogy, and morphology of geologic materials in northwestern Gale crater. Of particular interest is the stratigraphic record of a ∼5 km thick layered rock sequence exposed on the slopes of Aeolis Mons (also known as Mount Sharp). The instrument consists of three parts, a camera head mounted on the turret at the end of a robotic arm, an electronics and data storage assembly located inside the rover body, and a calibration target mounted on the robotic arm shoulder azimuth actuator housing. MAHLI can acquire in-focus images at working distances from ∼2.1 cm to infinity. At the minimum working distance, image pixel scale is ∼14 µm per pixel and very coarse silt grains can be resolved. At the working distance of the Mars Exploration Rover Microscopic Imager cameras aboard Spirit and Opportunity, MAHLI's resolution is comparable at ∼30 µm per pixel. Onboard capabilities include autofocus, autoexposure, sub-framing, video imaging, Bayer pattern color interpolation, lossy and lossless compression, focus merging of up to 8 focus stack images, white light and longwave ultraviolet (365 nm) illumination of nearby subjects, and 8 gigabytes of non-volatile memory data storage.

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Section: Lossy Compressed Datamentioning

confidence: 99%

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

et al. 2012

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“…The adaptive scales s obtained with the LPA-ICI filtering for this image (horizontal direction y 5 0 and y 5 p) are shown in Figures 4e and 4f. It is seen that the filterin (Hamilton and Adams, 1997), LI (Malvar et al, 2004), HD (Hirakawa and Parks, 2005), SA (Li, 2005), DFPD (Menon et al, 2007), AP (Gunturk et al, 2002), CCA , CCA1PP is a demosaicing approach with postprocessing (Lukac et al, 2004a), DLMMSE-based interpolation (Zhang and Wu, 2005), proposed LPA-ICI-based interpolationin, ''Oracle G'' is the proposed LPA-ICI-based interpolation with the optimal threshold parameter G. (Malvar et al, 2004), HD (Hirakawa and Parks, 2005a), SA (Li, 2005), DFPD (Menon et al, 2007), AP (Gunturk et al, 2002), CCA , CCA1PP is a demosaicing approach with postprocessing (Lukac et al, 2004a), DLMMSE-based interpolation (Zhang and Wu, 2005), proposed LPA-ICI interpolation, ''Oracle G'' is the proposed LPA-ICI interpolation with the optimal threshold parameter G. [Color figure can be viewed in the online issue, which is available at www.interscience. wiley.com.…”

Section: Resultsmentioning

confidence: 99%

“…The numerical results are summarized in Table I for each of 24 images and ordered in the ascending order of mean PSNR values (the last row), highlighting the best results with bold face. The diagram of mean PSNR values for each color (Hamilton and Adams, 1997), LI (Malvar et al, 2004), HD (Hirakawa and Parks, 2005), SA (Li, 2005), DFPD (Menon et al, 2007), AP (Gunturk et al, 2002), CCA , CCA1PP is a demosaicing approach with postprocessing (Lukac et al, 2004a), DLMMSE-based interpolation (Zhang and Wu, 2005), proposed LPA-ICI-based interpolationin, ''Oracle G'' is the proposed LPA-ICI-based interpolation with the optimal threshold parameter G.…”

Section: Resultsmentioning

confidence: 99%

“…The authors use the gradients of blue and red channels as the correction terms to interpolate the green channel. Similar idea is exploited effectively in (Laroche and Prescott, 1994;Wu and Zhang, 2004;Malvar et al, 2004) but with a different aggregation of vertical and horizontal estimates. Directional filtering with a posteriori decision is also effectively exploited for demosaicing in (Menon et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…We will classify the demosaicing techniques into two categories: noniterative (Laroche and Prescott, 1994;Hamilton and Adams, 1997;Adams, 1998;Taubman, 2000;Pei and Tam, 2003;Malvar et al, 2004;Zhang and Wu, 2005;Menon et al, 2007) and iterative (Kimmel, 1999;Gunturk et al, 2002;Lukac et al, 2004a;Hirakawa and Parks, 2005a;Li, 2005). There are also alternative ways of this classification, for instance considered in (Gunturk et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatially adaptive color filter array interpolation for noiseless and noisy data

Paliy

Katkovnik

Bilcu

et al. 2007

Int J Imaging Syst Tech

103

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Conventional single-chip digital cameras use color filter arrays (CFA) to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, to produce an RGB image. In this article, we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design, we assume that, following the concept proposed in (Zhang and Wu, IEEE Trans Image Process 14 (2005), 2167-2178), the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors that are considered as an additive noise. A specially designed signaladaptive filter is used to remove this so-called demosaicing noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals applied to select varying scales of LPA. This technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The presented CFA interpolation (CFAI) technique takes significant advantage from assuming that the original data is noise-free. Nevertheless, in many applications, the observed data is noisy, where the noise is treated as an important intrinsic degradation of the data. We develop an adaptation of the proposed CFAI for noisy data, integrating the denoising and CFAI into a single procedure. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise common for charge-coupled device and complementary-symmetry/metal-oxide semiconductor sensors. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real data.

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The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in‐flight calibration, validation, and data archiving

Fraeman

Godber

McNair

et al. 2017

Earth and Space Science

134

245

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The NASA Curiosity rover Mast Camera (Mastcam) system is a pair of fixed-focal length, multispectral, color CCD imagers mounted~2 m above the surface on the rover's remote sensing mast, along with associated electronics and an onboard calibration target. The left Mastcam (M-34) has a 34 mm focal length, an instantaneous field of view (IFOV) of 0.22 mrad, and a FOV of 20°× 15°over the full 1648 × 1200 pixel span of its Kodak KAI-2020 CCD. The right Mastcam (M-100) has a 100 mm focal length, an IFOV of 0.074 mrad, and a FOV of 6.8°× 5.1°using the same detector. The cameras are separated by 24.2 cm on the mast, allowing stereo images to be obtained at the resolution of the M-34 camera. Each camera has an eight-position filter wheel, enabling it to take Bayer pattern red, green, and blue (RGB) "true color" images, multispectral images in nine additional bands spanning~400-1100 nm, and images of the Sun in two colors through neutral density-coated filters. An associated Digital Electronics Assembly provides command and data interfaces to the rover, 8 Gb of image storage per camera, 11 bit to 8 bit companding, JPEG compression, and acquisition of high-definition video. Here we describe the preflight and in-flight calibration of Mastcam images, the ways that they are being archived in the NASA Planetary Data System, and the ways that calibration refinements are being developed as the investigation progresses on Mars. We also provide some examples of data sets and analyses that help to validate the accuracy and precision of the calibration.Plain Language Summary We describe the calibration and archiving of the images being obtained from the Mastcam multispectral, stereoscopic imaging system on board the NASA Curiosity Mars rover. Calibration is critical to detailed scientific analysis of instrumental data, and in this paper we not only describe the details of the calibration process and the steps in our resulting data calibration pipeline but also present some examples of the kinds of scientific analyses and discoveries that this calibration has enabled.

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High-quality linear interpolation for demosaicing of Bayer-patterned color images

Cited by 348 publications

References 8 publications

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

Spatially adaptive color filter array interpolation for noiseless and noisy data

The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in‐flight calibration, validation, and data archiving

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