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Underwater Single Image Restoration Using Dark Channel Prior

Authors: Felipe M. Codevilla; Silvia Silva Da Costa Botelho; Paulo Drews; Nelson Duarte Filho; Joel Felipe De Oliveira Gaya;

Underwater Single Image Restoration Using Dark Channel Prior

Abstract

The underwater vision is highly spoiled by the underwater degradation effects. As light propagates in the water or other participative mediums, it suffers from a substantial scattering effect that produces poor image quality. Based on a physical model that describes this phenomenon it is possible to recover an haze-free image. But, for this procedure to succeed, it is necessary to obtain certain parameters from the model. With an adaptation of the Dark Channel Prior, proposed by this paper, we are able to obtain a rough distance map estimative. With this, and some model simplifications, we are able to successfully obtain the restoration of the image.

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Average
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