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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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We present an approach to easily remove the effects of haze from images.

We present an approach for easily removing the effects of haze from passively acquired images. Our approach is based on the fact that usually the natural illuminating light scattered by atmospheric particles (airlight) is partially polarized. Optical filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, stems from physics-based analysis that works under a wide range of atmospheric and viewing conditions, even if the polarization is low. The approach does not rely on specific scattering models such as Rayleigh scattering and does not rely on the knowledge of illumination directions. It can be used with as few as two images taken through a polarizer at different orientations. As a byproduct, the method yields a range map of the scene, which enables scene rendering as if imaged from different viewpoints. It also yields information about the atmospheric particles. We present experimental results of complete dehazing of outdoor scenes, in far-from-ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color.

Vision and Bad WeatherCurrent vision algorithms assume that the radiance from a scene point reaches the observer unaltered. However, it is well known from atmospheric physics that the atmosphere scatters light energy radiating from scene points. Ultimately, vision systems must deal with realistic atmospheric conditions to be effective outdoors. Several models describing the visual manifestations of the atmosphere can be found in atmospheric optics (see [Mid52], [McC75]). These models can be exploited to not only remove bad weather effects, but also to recover valuable scene information. Surprisingly, little work has been done in computer vision on weather related issues. Cozman and Krotkov[CK97] computed depth cues from iso-intensity points. Nayar and Narasimhan[NN99] used well established atmospheric scattering models, namely, attenuation and airlight, to extract complete scene structure from one or two images, irre- spective of scene radiances. They also proposed a dichromatic atmospheric scattering model that describes the dependence of atmospheric scattering on wavelength. However, the algorithm they developed to recover structure using this model, requires a clear day image of the scene.In this paper, we develop a general chromatic framework for the analysis of images taken under poor weather conditions. The wide spectrum of atmospheric particles makes a general study of vision in bad weather hard. So, we limit ourselves to weather conditions that result from fog and haze. We begin by describing the key mechanisms of scattering. Next, we analyze the dichromatic model proposed in [NN99], and experimentally verify it for fog and haze. Then, we derive several useful geometric constraints on scene color changes due to different but unknown atmospheric conditions. Finally, we develop algorithms to compute fog or haze color, to construct depth maps of arbitrary scenes, and to recover scene colors as they would appear on a clear day. All of our methods only require images of the scene taken under two or more poor weather conditions, and not a clear day image of the scene. Mechanisms of ScatteringThe interactions of light with the atmosphere can be broadly classified into three categories, namely, scattering, absorption and emission. Of these, scattering due to suspended atmospheric particles is most pertinent to us. For a detailed treatment of the scattering patterns and their relationship to particle shapes and sizes, we refer the reader to the works of [Mid52] and [Hul57]. Here, we focus on the two fundamental scattering phenomena, namely, airlight and attenuation, which form the basis of our framework. AirlightWhile observing an extensive landscape, we quickly notice that the scene points appear progressively lighter as our attention shifts from the foreground toward the horizon. This phenomenon, known as airlight (see [Kos24]), results from the scattering of environmental light toward the observer, by the atmospheric particles within the observer's cone of vision.

Abstract. Detecting shadows from images can significantly improve the performance of several vision tasks such as object detection and tracking. Recent approaches have mainly used illumination invariants which can fail severely when the qualities of the images are not very good, as is the case for most consumer-grade photographs, like those on Google or Flickr. We present a practical algorithm to automatically detect shadows cast by objects onto the ground, from a single consumer photograph. Our key hypothesis is that the types of materials constituting the ground in outdoor scenes is relatively limited, most commonly including asphalt, brick, stone, mud, grass, concrete, etc. As a result, the appearances of shadows on the ground are not as widely varying as general shadows and thus, can be learned from a labelled set of images. Our detector consists of a three-tier process including (a) training a decision tree classifier on a set of shadow sensitive features computed around each image edge, (b) a CRF-based optimization to group detected shadow edges to generate coherent shadow contours, and (c) incorporating any existing classifier that is specifically trained to detect grounds in images. Our results demonstrate good detection accuracy (85%) on several challenging images. Since most objects of interest to vision applications (like pedestrians, vehicles, signs) are attached to the ground, we believe that our detector can find wide applicability.

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