Abstract
Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).
Highlights
Images acquired by the sensor in digital cameras need to be processed for being displayed on an output screen
Noise can be amplified by the processing operations, the amplification at the output image depends both on physical properties of the sensor and the kind of processing applied to the raw image [1,2,3,4,5]
This paper was focused on the impact of the spectral sensitivities of imaging sensors on noise of output image
Summary
Images acquired by the sensor in digital cameras need to be processed for being displayed on an output screen. Because of the random absorption of photons, image capture is always subject to noise. Noise can be amplified by the processing operations, the amplification at the output image depends both on physical properties of the sensor (spectral sensitivities, dynamic range, etc.) and the kind of processing applied to the raw image [1,2,3,4,5]. The question of sensor optimization is very complex, because it is a spatio-spectral inverse problem [7]. The problem involves the choice of spectral sensitivities associated to color channels and their distribution on the color filter array (CFA), for optimizing both color rendering and spatial quality
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