Gradient-based correction of chromatic aberration in the joint acquisition of color and near-infrared images

Abstract

Chromatic aberration distortions such as wavelength-dependent blur are caused by imperfections in photographic lenses. These distortions are much more severe in the case of color and near-infrared joint acquisition, as a wider band of wavelengths is captured. In this paper, we consider a scenario where the color image is in focus, and the NIR image captured with the same lens and same focus settings is out-of-focus and blurred. To reduce chromatic aberration distortions, we propose an algorithm that estimates the blur kernel and deblurs the NIR image using the sharp color image as a guide in both steps. In the deblurring step, we retrieve the lost details of the NIR image by exploiting the sharp edges of the color image, as the gradients of color and NIR images are often correlated. However, differences of scene reflections and light in visible and NIR bands cause the gradients of color and NIR images to be different in some regions of the image. To handle this issue, our algorithm measures the similarities and differences between the gradients of the NIR and color channels. The similarity measures guide the deblurring algorithm to efficiently exploit the gradients of the color image in reconstructing high-frequency details of NIR, without discarding the inherent differences between these images. Simulation results verify the effectiveness of our algorithm, both in estimating the blur kernel and deblurring the NIR image, without producing ringing artifacts inherent to the results of most deblurring methods.