Unlike photographic image sensors with infrared cutoff filter, low light image sensors gather light over visible and near infrared (VIS-NIR) spectrum to improve sensitivity. However, removing infrared cutoff filter makes the color rendering challenging. In addition, no color chart, with calibrated infrared content, is available to compute color correction matrix (CCM) of such sensors. In this paper we propose a method to build a synthetic color chart (SCC) to overcome this limitation. The choice of chart patches is based on a smart selection of spectra from open access and our own VIS-NIR hyperspectral images databases. For that purpose we introduce a fourth cir dimension to CIE-L*a*b* space to quantify the infrared content of each spectrum. Then we uniformly sample this L*a*b*cir space, leading to 1498 spectra constituting our synthetic color chart. This new chart is used to derive a 3x4 color correction matrix associated to the commercial RGB-White sensor (Teledyne-E2V EV76C664) using a classical linear least square minimization.. We show an improvement of signal to noise ratio (SNR) and color accuracy at low light level compared to standard CCM derived using Macbeth color chart.
KEYWORDS: High dynamic range imaging, Digital cameras, Image processing, Cameras, Process modeling, Sensors, Gaussian filters, RGB color model, Data conversion, Image compression
We propose a complete digital camera workflow to capture and render
high dynamic range (HDR) static scenes, from RAW sensor data to an
output-referred encoded image. In traditional digital camera
processing, demosaicing is one of the first operations done after
scene analysis. It is followed by rendering operations, such as
color correction and tone mapping. In our workflow, which is based
on a model of retinal processing, most of the rendering steps are
performed before demosaicing. This reduces the complexity of the
computation, as only one third of the pixels are processed. This is
especially important as our tone mapping operator applies local and
global tone corrections, which is usually needed to well render high
dynamic scenes. Our algorithms efficiently process HDR images with
different keys and different content.
In this paper, we restate the model of spatio-chromatic sampling in single-chip digital cameras covered by Color Filter
Array (CFA)1. The model shows that a periodic arrangement of chromatic samples in the CFA gives luminance and
chromatic information that is localized in the Fourier domain. This representation allows defining a space invariant
uniform demosaicking method which is based on the frequency selection of the luminance and chrominance information.
We then show two extended methods which used the frequency representation of the Bayer CFA2,3 to derive adaptive
demosaicking. Finally, we will show the application of the model for CFA with random arrangement of chromatic
samples, either using a linear method based on Wiener estimation4 or with an adaptive method5.
We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images
sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data before interpolation artifacts are introduced. After image registration, a high resolution color image is reconstructed at once using the full set of images. For this, we use normalized
convolution, an image interpolation method from a nonuniform set of samples. Our algorithm is tested and
compared to other approaches in simulations and practical experiments.
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