Color perception through atmospheric haze

Henry, Ronald C.; Mahadev, Shudeish; Urquijo, Santiago; Chitwood, Derek E.

doi:10.1364/josaa.17.000831

Cited by 55 publications

(35 citation statements)

References 5 publications

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“…These levels of inherent colorfulness represent a reasonable range for natural targets. 12 As might be expected, more colorful objects are more sensitive to changes in atmospheric haze. Perhaps unexpectedly, the figure shows that landscape features at a distance corresponding to an optical depth of 1-2 are the most sensitive to changes in extinction as measured by deciviews.…”

Section: Jnd In Colorfulnessmentioning

confidence: 90%

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Just-Noticeable Differences in Atmospheric Haze

Henry

2002

Journal of the Air & Waste Management Association

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This article examines the only available experimental data taken in the natural environment on the ability of an observer to perceive small, incremental changes in the colorfulness of objects seen through atmospheric haze and estimates an appropriate just-noticeable difference (JND) from these data. This experimentally determined threshold of perception is compared to changes in the deciview scale. Based on these experimental results, the deciview scale is found to not be uniform over a wide range of visibility conditions, as has been previously claimed. In addition, a 1-deciview change never produces a perceptible change in haze, as defined by a 95% probability of producing a measurable change in the colorfulness of an object seen through the haze.

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Section: Jnd In Colorfulnessmentioning

confidence: 90%

“…[9][10][11] However, the SEAVS had a number of other aspects, including a study of the perception of color through atmospheric haze. 12 The methods and primary results of the color perception study are described below.…”

Section: Experimental Datamentioning

confidence: 99%

Just-Noticeable Differences in Atmospheric Haze

Henry

2002

Journal of the Air & Waste Management Association

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“…One important correlation for dehazing algorithms constitutes the relation between optical depth and airlight [18,11]. The airlight A becomes more dominant as the optical depth increases.…”

Section: Airlight Color (A ∞ ) Estimationmentioning

confidence: 99%

A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image

Ancuti

Hermans

Bekaert

2011

Lecture Notes in Computer Science

147

116

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Abstract. In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach.

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“…According to [5], A/D = 1 for objects that have reflectance similar to a medium gray card at b = 0.2. Using these relations in Eqs.…”

Section: The Airlight Dominancementioning

confidence: 99%