Haze is an undesirable effect in images caused when atmospheric particles, such as water droplets, ice crystals, dust, or smoke, are lit directly or indirectly by the sun. This effect can be counteracted by image processing, to bring back the details of a hazy image. Unfortunately, the execution time is often long, which prevents deployment in some video or real-time applications. In this paper, we propose to tune several parameters of the Dark Channel Prior method (DCP) algorithm combined with the fast guided filter. We evaluate the optimization in terms of execution time, and quantify the output image quality using different image quality metrics.
Spectral and polarization imaging (SPI) is an emerging sensing method that permits the analysis of both spectral and polarization information of a scene. The existing acquisition systems are limited by several factors, such as the space requirement, the inability to capture quickly the information, and the high cost. We propose an SPI acquisition system with a high spatial resolution that combines six spectral channels and four polarization channels. The optical setup employs two color-polarization filter array cameras and a pair of bandpass filters. We define the processing pipeline, consisting of preprocessing, geometric calibration, spectral calibration, and data transformation. We show that, around specular highlights, the spectral reconstruction can be improved by filtering the polarized intensity. We provide a database of 28 spectropolarimetric scenes with different materials for future simulation and analysis by the research community.
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