Correlation-based deblurring leveraging multispectral chromatic aberration in color and near-infrared joint acquisition

Abstract

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.

AAM: AN Assessment Metric of Axial Chromatic Aberration

“…The opposite is true if high frequency spectral fusion is desired, (e.g. depth-of-field extension or deblurring), where axial aberration can be leveraged [2,8,9]. We can visually verify in Fig.…”

“…Chromatic aberrations are caused by dispersion, as the refractive index of a lens is non-linearly wavelength dependent. Every acquired wavelength λ has a different refractive index n(λ) and is thus deviated by a different angle [1,2]. The result is that every wavelength has its own focal length f (λ) and power P (λ) given by the lens makers' formula [3]:…”

“…Chromatic aberration has undesirable visual artifacts [4][5][6], and can be a challenge to multispectral applications where spatial matching between channels is important [7]. However, it can also be useful in deblurring for multispectral systems having acquisition channels with shifted focus depth [2,8,9] by transferring high frequencies from one channel to another. Chromatic aberrations are either axial (longitudinal) or lateral (transverse) chromatic aberrations ( Fig.…”

Solving the depth ambiguity in single-perspective images

“…Unlike stereo, it does not have to cope with correspondence problems, and only requires a single capture. This has yielded many techniques for estimating image blur [9][10][11][12][18][19][20][21][22][23] and mapping it to depth. Lin et al propose in [12] to compute multiple aperture-shape filters that they use to distinguish defocus levels in a given image.…”

“…We compute ∆ R,G and ∆ N I R,G for every object in our dataset by subtracting the blur estimates obtained using the following methods: Hu [11], Zucker [9], Crété [36] and El Helou [20]. The blur estimation techniques perform best on edge locations, therefore, instead of interpolating the results to the rest of the image, which does not provide additional information, we assess accuracy solely over edge locations.…”