Haze detection and removal in high resolution satellite image with wavelet analysis. An improved dark-object subtraction technique for atmonspheric scattering correction of multispectral data. In Proceedings of the Conference on Digital Image Computing: Techniques and Applications (DICTA'09). Improved single image dehazing using geometry. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information.Īn extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible.
#COLOR LINES PATCH#
The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene.
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