Overcast Sky Luminances and Directional Luminous Reflectances of Objects and Backgrounds under Overcast Skies

Gordon, Jacqueline I.; Church, Peggy V.

doi:10.1364/ao.5.000919

Cited by 17 publications

(6 citation statements)

References 3 publications

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“…1. The sky is assumed to be mostly cloudy or overcast and that the radiance from the sky varies smoothly with respect to the polar and azimuth angles of the hemisphere [8], [11], [20]. For more details, we refer the reader to [21].…”

Section: Attenuation and Airlightmentioning

confidence: 99%

Contrast restoration of weather degraded images

Narasimhan

Nayar

2003

IEEE Trans. Pattern Anal. Machine Intell.

1,408

414

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Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. In this paper, we present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera.

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Section: Attenuation and Airlightmentioning

confidence: 99%

Contrast restoration of weather degraded images

Narasimhan

Nayar

2003

IEEE Trans. Pattern Anal. Machine Intell.

1,408

414

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“…Daylight spectrum has also been represented using a set of linear bases [25]. Works that model clouds and their effect on the ambient illumination also exist in literature [26,27]. In graphics, scenes have been rendered under different daylight [28] and night illuminations [29].…”

Section: Variation In Illuminationmentioning

confidence: 99%

All the Images of an Outdoor Scene

Narasimhan

Wang

Nayar

2002

Lecture Notes in Computer Science

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Abstract. The appearance of an outdoor scene depends on a variety of factors such as viewing geometry, scene structure and reflectance (BRDF or BTF), illumination (sun, moon, stars, street lamps), atmospheric condition (clear air, fog, rain) and weathering (or aging) of materials. Over time, these factors change, altering the way a scene appears. A large set of images is required to study the entire variability in scene appearance. In this paper, we present a database of high quality registered and calibrated images of a fixed outdoor scene captured every hour for over 5 months. The dataset covers a wide range of daylight and night illumination conditions, weather conditions and seasons. We describe in detail the image acquisition and sensor calibration procedures. The images are tagged with a variety of ground truth data such as weather and illumination conditions and actual scene depths. This database has potential implications for vision, graphics, image processing and atmospheric sciences and can be a testbed for many algorithms. We describe an example application -image analysis in bad weather -and show how this method can be evaluated using the images in the database. The database is available online at http://www.cs.columbia.edu/CAVE/. The data collection is ongoing and we plan to acquire images for one year. Variability in Scene AppearanceThe appearance of a fixed scene depends on several factors -the viewing geometry, illumination geometry and spectrum, scene structure and reflectance (BRDF or BTF) and the medium (say, atmosphere) in which the scene is immersed. The estimation of one or more of these appearance parameters from one or more images of the scene has been an important part of research in computer vision. Several researchers have focused on solving this inverse problem under specific conditions of illumination (constant or smoothly varying), scene structure (no discontinuities), BRDF (lambertian) and transparent media (pure air). Images captured to evaluate their methods adhere to the specific conditions. While understanding each of these specific cases is important, modeling scene appearance in the most general setting is ultimately the goal of a vision system. To model, develop and evaluate such a general vision system, it is critical to collect a comprehensive set of images that describes the complete variability in the appearance of a scene. Several research groups have collected images of a scene (for example, faces, textures, objects) under varying lighting conditions

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“…Yet doubts persist about it, ranging from suggestions that thick overcasts have nearly uniform luminances [14], to observations that some overcasts' brightness maxima are in fact not at the zenith [15][16][17], to an empirical model that predicts some slight zenith asymmetry even for thick overcasts [18,19]. More recently, the CIE has incorporated some of the asymmetry from [18] to allow for brightness maxima around the Sun in optically thin overcasts [20], as do several other empirical models [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Observed brightness distributions in overcast skies

Lee

Devan

2008

Appl. Opt.

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Beneath most overcasts, clouds' motions and rapidly changing optical depths complicate mapping their angular distributions of luminance L v and visible-wavelength radiance L. Fisheye images of overcast skies taken with a radiometer-calibrated digital camera provide a useful new approach to solving this problem. Maps calculated from time-averaged images of individual overcasts not only show their brightness distributions in unprecedented detail, but they also help solve a long-standing puzzle about where brightness maxima of overcasts are actually located. When combined with simulated radiance distributions from MODTRAN4, our measured radiances also let us estimate the gradients of cloud thickness observed in some overcasts.

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Overcast Sky Luminances and Directional Luminous Reflectances of Objects and Backgrounds under Overcast Skies

Cited by 17 publications

References 3 publications

Contrast restoration of weather degraded images

Contrast restoration of weather degraded images

All the Images of an Outdoor Scene

Observed brightness distributions in overcast skies

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