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.

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

“…Out-of-focus NIR images can be deblurred through postprocessing [14][15][16]. In [17], Sadeghipoor et al suggest that the gradients of the luma channel Y (the pixel-wise average of color channels) and of the NIR image representing the same scene are generally similar.…”

Detailed geological mapping in mountain areas using an unmanned aerial vehicle: application to the Rodoretto Valley, NW Italian Alps

“…60 ha/h), unmanned aerial vehicles (UAVs) can acquire data more rapidly and less expensively than typical airborne surveys. Commercial UAVs (Sadeghipoor et al 2015) typically acquire RGB (Endres et al 2014) and nearinfrared (NIR) images (Chen et al 2015). Using a photogrammetric approach, digital surface models (DSMs), digital terrain models (DTMs) (Dabove et al 2015) and orthophotos (Guarnieri et al 2015) can be generated automatically.…”

Image Restoration Considering Chromatic Aberration Problem of Multi-Spectral Filter Array Image

“…더 나아가서, 컬러 대역과 근적외선 필터를 각 픽셀에 설치한 이미지 센서(complementary metal oxide semiconductor: CMOS) 가 연구되었다 [12] . [14] . 또한 채널 간의 상관관계를 기반으로 하여 컬러 보간시 디블러널 간의 상관관계를 기반으로 하여 컬러 보간시 디블러링 (deblurring)을 함께 수행하는 알고리즘이 있다 [15] .…”