Color correction is one of the most essential camera imaging operations that transforms a camera-specific RGB color space to a standard color space, typically the XYZ or the sRGB color space. Linear color correction (LCC) and polynomial color correction (PCC) are two widely used methods; they perform the color space transformation using a color correction matrix. Owing to the use of high-order terms, PCC generally achieves lower colorimetric errors than LCC. However, PCC amplifies noise more severely than LCC. Consequently, for noisy images, there exists a trade-off between LCC and PCC regarding color fidelity and noise amplification. We propose a color correction framework called tunable color correction (TCC) that enables us to tune the color correction matrix between the LCC and the PCC models. We also derive a mean squared error calculation model of PCC that enables us to select the best trade-off balance in the TCC framework. We experimentally demonstrate that TCC effectively balances the trade-off for noisy images and outperforms LCC and PCC. We also generalize TCC to multispectral cases and demonstrate its effectiveness by taking the color correction for an RGB-near-infrared sensor as an example.
Extra band information in addition to the RGB, such as the near-infrared (NIR) and the ultra-violet, is valuable for many applications. In this paper, we propose a novel color filter array (CFA), which we call “hybrid CFA," and a demosaicking algorithm for the simultaneous capturing of the RGB and the additional band images. Our proposed hybrid CFA and demosaicking algorithm do not rely on any specific correlation between the RGB and the additional band. Therefore, the additional band can be arbitrarily decided by users. Experimental results demonstrate that our proposed demosaicking algorithm with the proposed hybrid CFA can provide the additional band image while keeping the RGB image almost the same quality as the image acquired by using the standard Bayer CFA.
A color difference interpolation technique is widely used for color image demosaicking. In this paper, we propose
a minimized-laplacian residual interpolation (MLRI) as an alternative to the color difference interpolation, where
the residuals are differences between observed and tentatively estimated pixel values. In the MLRI, we estimate
the tentative pixel values by minimizing the Laplacian energies of the residuals. This residual image transfor-
mation allows us to interpolate more easily than the standard color difference transformation. We incorporate
the proposed MLRI into the gradient based threshold free (GBTF) algorithm, which is one of current state-of-
the-art demosaicking algorithms. Experimental results demonstrate that our proposed demosaicking algorithm
can outperform the state-of-the-art algorithms for the 30 images of the IMAX and the Kodak datasets.
Spectral reflectance is an inherent property of objects that is useful for many computer vision tasks. The spectral
reflectance of a scene can be described as a spatio-spectral (SS) datacube, in which each value represents the
reflectance at a spatial location and a wavelength. In this paper, we propose a novel method that reconstructs
the SS datacube from raw data obtained by an image sensor equipped with a multispectral filter array. In our
proposed method, we describe the SS datacube as a linear combination of spatially adaptive SS basis vectors.
In a previous method, spatially invariant SS basis vectors are used for describing the SS datacube. In contrast,
we adaptively generate the SS basis vectors for each spatial location. Then, we reconstruct the SS datacube
by estimating the linear coefficients of the spatially adaptive SS basis vectors from the raw data. Experimental
results demonstrate that our proposed method can accurately reconstruct the SS datacube compared with the
method using spatially invariant SS basis vectors.
Multispectral imaging is highly demanded for precise color reproduction and for various computer vision applications.
Multispectral imaging with a multispectral color filter array (MCFA), which can be considered as a
multispectral extension of commonly used consumer RGB cameras, could be a simple, low-cost, and practical
system. A challenge of the multispectral imaging with the MCFA is multispectral demosaicking because each
spectral component of the MCFA is severely undersampled. In this paper, we propose a novel multispectral
demosaicking algorithm using a guided filter. The guided filter is recently proposed as an excellent structurepreserving
filter. The guided filter requires so-called a guide image. A main issue of the guided filter is how to
obtain an effective guide image. In our proposed algorithm, we generate the guide image from the most densely
sampled spectral component in the MCFA. Then, ohter spectral components are interpolated by the guided
filter. Experimental results demonstrate that our proposed algorithm outperforms other existing demosaicking
algorithms both visually and quantitatively.