Near-Infrared Fusion for Photorealistic Image Dehazing

Scattering of light due to the presence of aerosol particles along the path of radiation causes atmospheric haze in images. This scattering is significantly less severe in longer wavelength bands than in shorter ones, thus the importance of near-infrared (NIR) information for dehazing color images. This paper first presents an adaptive hyperspectral al- gorithm that analyzes intensity inconsistencies across spectral bands. It then leverages the algorithm’s results to preserve photorealism of the visible color image during the dehazing. The color images are dehazed through a hyperspectral fusion of color and NIR images, taking into account any inconsistencies that can affect the photorealism. Our dehazing results on real images contain no halo or aliasing artifacts in hazy regions and successfully preserve the color image elsewhere

AAM: An Assessment Metric of Axial Chromatic Aberration

Knowledge of lens specifications is important to identify the best lens for a given capture scenario and application. Lens manufacturers provide many specifications in their data sheets, and multiple initiatives for testing and comparing different lenses can be found online. However, due to the lack of a suitable metric or technique, no evaluation of axial chromatic aberration is available. In this paper, we propose a metric, Axial Aberration Magnitude or AAM, that assesses the degree of axial chromatic aberration of a given lens. Our metric is generalizable to multispectral acquisition systems and is very simple and cheap to compute. We present the entire procedure and algorithm for computing the AAM metric, and evaluate it for two spectral systems and two consumer lenses