Joint processing of visible (RGB) and near-infrared (NIR) images has recently found some appealing applications, which make joint capturing a pair of visible and NIR images an important problem. In this paper, we propose a new method to design color filter arrays (CFA) and demosaicing matrices for acquiring NIR and visible images using a single sensor. The proposed method modifies the optimum CFA algorithm proposed in [1] by taking advantage of the NIR/visible correlation in the design process. Simulation results show that by applying the proposed method, the quality of demosaiced NIR and visible images is increased by about 1 dB in peak signal-to-noise ratio over the results of the optimum CFA algorithm. It is also shown that better visual quality can be obtained by using the proposed algorithm.
Sensors of most digital cameras are made of silicon that is inherently sensitive to both the visible and near-infrared parts of the electromagnetic spectrum. In this paper, we address the problem of color and NIR joint acquisition. We propose a framework for the joint acquisition that uses only a single silicon sensor and a slightly modified version of the Bayer color-filter array that is already mounted in most color cameras. Implementing such a design for an RGB and NIR joint acquisition system requires minor changes to the hardware of commercial color cameras.One of the important differences between this design and the conventional color camera is the post-processing applied to the captured values to reconstruct full resolution images. By using a CFA similar to Bayer, the sensor records a mixture of NIR and one color channel in each pixel. In this case, separating NIR and color channels in different pixels is equivalent to solving an under-determined system of linear equations. To solve this problem, we propose a novel algorithm that uses the tools developed in the field of compressive sensing. Our method results in high-quality RGB and NIR images (the average PSNR of more than 30 dB for the reconstructed images) and shows a promising path towards RGB and NIR cameras.
Chromatic aberration, caused by photographic lens imperfections, results in the image of only one spectral channel being sharp, while the other channels are blurred depending on their wavelengths difference with the sharp channel.We study chromatic aberration for a system that jointly records color and near-infrared (NIR) images on a single sensor. Chromatic aberration in such a system leads to a blurred NIR image when the color image is in-focus and sharp. We propose an algorithm that deblurs the NIR image using the gradients of the sharp color image, as both scene representations are generally similar. However, the details of these images often exhibit significant differences due to varying scene reflection and absorption in the corresponding bands. To account for this, we compute the correlation between color and NIR gradients, and use the gradients of the color image in reconstructing NIR only where the gradients are highly correlated. We propose a multiscale scheme that gradually deblurs NIR and accurately computes similarities between color and NIR gradients. Experimental results show that our algorithm recovers details of NIR without producing visible artifacts.
Joint acquisition of color and near-infrared (NIR) images is of growing interest due to various applications that make use of the additional spectral information. An obstacle to this acquisition is the wavelength-dependent blurring caused by the chromatic aberration of optical lenses. When one of the spectral channels, for example the green channel, is in focus on the sensor plane, the images of the other channels, especially NIR, are blurred. This paper presents a study of spectral-spatial correlations between color and NIR channels and proposes a method to correct for chromatic aberrations. The algorithm we introduce leverages axial chromatic aberration to deblur the NIR image when the color image is in focus. The proposed technique improves image sharpness by 48.8% on average compared to state-of-the-art results. Moreover, our method generates an NIR image that has a larger depth-of-field compared to an NIR image originally captured in focus.
A cost-effective and convenient approach for color imaging is to use a single sensor and mount a color filter array (CFA) in front of it, such that at each spatial position the scene information in only one color channel is captured. To estimate the missing colors at each pixel, a demosaicing algorithm is applied to the CFA samples. Besides the filter arrangement and the demosaicing method, the spectral sensitivity functions of the CFA filters considerably affect the quality of the demosaiced image. In this paper, we propose an algorithm to compute the optimum spectral sensitivities of filters in the single sensor imager. The proposed algorithm solves a constrained optimization problem to find optimum spectral sensitivities and the corresponding linear demosaicing method. An important constraint for this problem is the smoothness of spectral sensitivities, which is imposed by modeling these functions as a linear combination of several smooth kernels. Simulation results verify the effectiveness of the proposed algorithm in finding optimal spectral sensitivity functions, which outperform measured camera sensitivity functions.
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