Abstract
Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.
Highlights
Outdoor images are mostly distorted by the turbid medium of dust, water vapors, and various airborne particles
The proposed method is based on the observation that a few hundred distant colors can be used to approximat colors present in an image [47]
The two end points of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight
Summary
Outdoor images are mostly distorted by the turbid medium of dust, water vapors, and various airborne particles. Haze introduces two type of distortions in the radiance of a scene point: 1) visibility is attenuated, and 2) a semi-transparent layer of ambient light, known as airlight is added. This physical phenomenon is represented by the following image formation model [41, 42]. The proposed method is based on the observation that a few hundred distant colors can be used to approximat colors present in an image [47] We generalize this concept over a collection of natural hazefree images to learn their color-palette. It achieves quiet good results for a variety of images and is competitive with other state-of-the-art methods
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