&lt;title&gt;AOTF polarization difference imaging&lt;/title&gt;

2005

SPIE Proceedings

Recent studies have shown that major visibility degradation effects caused by haze can be corrected for by analyzing polarization-filtered images. The analysis is based on the fact that the path-radiance in the atmosphere (airlight) is often partially polarized. Thus, associating polarization with path-radiance enables its removal, as well as compensation for atmospheric attenuation. However, prior implementations of this method suffered from several problems. First, they were based on mechanical polarizers, which are slow and rely on moving part. Second, the method had failed in image areas corresponding to specular objects, such as water bodies (lakes) and shiny construction materials (e.g., windows). The reason for this stems from the fact that specular objects reflect partially polarized light, confusing a naive association of polarization solely with path-radiance. Finally, prior implementations derived necessary polarization parameters by manually selecting reference points in the field of view. This human intervention is a drawback, since we would rather automate the process. In this paper, we report our most recent progress in the development of our visibility-improvement method. We show directions by which those problems can be overcome. Specifically, we added algorithmic steps which automatically extract the polarization parameters needed, and make visibility recovery more robust to polarization effects originating from specular objects. In addition, we now test an electrically-switchable polarizer based on a liquid crystal device for improving acquisition speed.

Section: Introductionmentioning

confidence: 99%

Advanced visibility improvement based on polarization filtered images

Namer

2005

SPIE Proceedings

“…Yet, simple optical filtering [54], [55] may have a limited 2 effect, indicating that some postprocessing is needed, based on the acquisition of both components of polarized light [54]. One approach is based on a simple subtraction of the differently polarization-filtered images [2], [39], or displays the degree of polarization (DOP) [5], [46]. That approach has fundamental dis- 2 The experiments described in [55] were based on subjective responses of divers, rather than on photographs.…”

Section: Relation To Previous Methodsmentioning

confidence: 99%

“…This observation has recently been utilized to enhance visibility in a perpendicular illumination setting [58]. Nevertheless, enhancement methods such as those used in [2], [5], [39], [46], and [58] are far from inverting the image formation process and recovering the objects. In contrast, our approach inverts the physical model in general natural lighting, thus the recovered image is similar to clear visibility appearance.…”

Section: Relation To Previous Methodsmentioning

confidence: 99%

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Recovery of Underwater Visibility and Structure by Polarization Analysis

Karpel

2005

IEEE J. Oceanic Eng.

532

263

Abstract-Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.Index Terms-Color, illumination, image enhancement, inverse problems, polarized light, scattering, three-dimensional reconstruction, undersea vision, underwater imaging. I. UNDERWATER VISION UNDERWATER vision is plagued by poor visibility conditions [1]- [6]. According to [7], most computer vision methods (e.g., those based on stereo triangulation or on structure from motion) cannot be employed directly underwater. This is due to the particularly challenging environmental conditions that complicate image matching and analysis. It is important to alleviate these visibility problems since underwater imaging is widely used in scientific research and technology. Computer vision methods are being used in this mode of imaging for various applications [5] What makes underwater imaging so problematic? To understand the challenge, consider Fig. 1. which shows an underwater archaeological site about 2.5-m deep. It is easy to see that visibility degradation effects vary as distances to objects increase [3], [28]. Since objects in the field of view (FOV) are at different distances from the camera, the causes for image degradation are spatially varying. This situation is analogous to open-air vision in bad weather (fog or haze) described in [29]-[34]. Contrary to this fact, traditional image enhancement tools, e.g., high pass filtering and histogram equalization, are typically spatially invariant. Since they do not model the spatially varying distance dependencies, traditional methods are of limited utility in countering visibility problems, as has been demonstrated in past experiments [33], [35] as well as in this paper.In this paper, we develop a physics-based approach for recovery of visibility when imaging underwater scenes in natural illumination. Since it is based on the models of image formation, the approach automatically accounts for dependencies on object distance and estimates a distance map of the scene as a by-product. The approach is fast and relies on raw images taken through different states of a polarizing filter. 1 These raw images have...

“…It has also been used for removal of scattering effects [11], [17], [46], e.g., in haze [35], underwater [9], [18], [33], [45], [52], and tissue [10]. The polarization state has four degrees of freedom, parameterized, for example, by the Stokes parameters.…”

Section: Introductionmentioning

confidence: 99%

Generalized mosaicing: polarization panorama

IEEE Trans. Pattern Anal. Machine Intell.

Nayar

2005

Abstract-We present an approach to image the polarization state of object points in a wide field of view, while enhancing the radiometric dynamic range of imaging systems by generalizing image mosaicing. The approach is biologicallyinspired, as it emulates spatially varying polarization sensitivity of some animals. In our method, a spatially varying polarization and attenuation filter is rigidly attached to a camera. As the system moves, it senses each scene point multiple times, each time filtering it through a different filter polarizing angle, polarizance, and transmittance. Polarization is an additional dimension of the generalized mosaicing paradigm, which has recently yielded high dynamic range images and multispectral images in a wide field of view using other kinds of filters. The image acquisition is as easy as in traditional image mosaics. The computational algorithm can easily handle nonideal polarization filters (partial polarizers), variable exposures, and saturation in a single framework. The resulting mosaic represents the polarization state at each scene point. Using data acquired by this method, we demonstrate attenuation and enhancement of specular reflections and semireflection separation in an image mosaic.