The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.
can also be used to remove reflections. Several methods propose different ways to estimate the relative motion of the reflected and transmitted image, which can be used to separate them @cite_25 @cite_12 @cite_29 @cite_28 @cite_27 . It is important to note that these methods assume static scenes---the motion is the apparent motion of the reflected layer relative to the transmitted layer, not scene motion. Other than that, these methods make assumptions that are less stringent than those made by single-image methods. Nonetheless, these algorithms work well when reflected and transmitted scenes are shallow in terms of depth, so that their velocity can be assumed uniform. For the case of spatially and temporally varying mixes, Kaftory and Zeevi propose to use sparse component analysis instead @cite_15 .
@cite_28 @cite_29 @cite_27 @cite_15 @cite_25 @cite_12
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We address the problem of estimating the latent image of a static bilayer scene (consisting of a foreground and a background at different depths) from motion blurred observations captured with a handheld camera. The camera motion is considered to be composed of in-plane rotations and translations. Since the blur at an image location depends both on camera motion and depth, deblurring becomes a difficult task. We initially propose a method to estimate the transformation spread function (TSF) corresponding to one of the depth layers. The estimated TSF (which reveals the camera motion during exposure) is used to segment the scene into the foreground and background layers and determine the relative depth value. The deblurred image of the scene is finally estimated within a regularization framework by accounting for blur variations due to camera motion as well as depth. This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene–called eoru motions –through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix . First, the ego motion is segmented and then several hypotheses are generated for the eoru motions . All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset ( Hopkins 155 ) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community. In the context of extracting information from video, bad weather conditions like rain can have a detrimental effect. In this paper, a novel framework to detect and remove rain streaks from video is proposed. The first part of the proposed framework for rain removal is a technique to detect rain streaks based on phase congruency features. The variation of features from frame to frame is used to estimate the candidate rain pixels in a frame. In order to reduce the number of false candidates due to global motion, frames are registered using phase correlation. The second part of the proposed framework is a novel reconstruction technique that utilizes information from three different sources, which are intensities of the rain affected pixel, spatial neighbors, and temporal neighbors. An optimal estimate for the actual intensity of the rain affected pixel is made based on the minimization of registration error between frames. An optical flow technique using local phase information is adopted for registration. This part of the proposed framework for removing rain is modeled such that the presence of local motion will not distort the features in the reconstructed video. The proposed framework is evaluated quantitatively and qualitatively on a variety of videos with varying complexities. The effectiveness of the algorithm is quantitatively verified by computing a no-reference image quality measure on individual frames of the reconstructed video. From a variety of experiments that are performed on output videos, it is shown that the proposed technique performs better than state-of-the-art techniques. Many natural images contain reflections and transparency, i.e., they contain mixtures of reflected and transmitted light. When viewed from a moving camera, these appear as the superposition of component layer images moving relative to each other. The problem of multiple motion recovery has been previously studied by a number of researchers. However no one has yet demonstrated how to accurately recover the component images themselves. In this paper we develop an optimal approach to recovering layer images and their associated motions from an arbitrary number of composite images. We develop two different techniques for estimating the component layer images given known motion estimates. The first approach uses constrained least squares to recover the layer images. The second approach iteratively refines lower and upper bounds on the layer images using two novel compositing operations, namely minimum- and maximum-composites of aligned images. We combine these layer extraction techniques with a dominant motion estimator and a subsequent motion refinement stage. This results in a completely automated system that recovers transparent images and motions from a collection of input images.