14 accepted papers
Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog,…
Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, w…
An exact method of correcting the rolling shutter (RS) effect requires recovering the underlying geometry, i.e. the scene structures and the camera motions between scanlines or between views. However, the multiple-view geometry for RS cameras is much more complicated than its global shutter (GS) cou…
Optical flow in heavy rainy scenes is challenging due to the presence of both rain steaks and rain veiling effect, which break the existing optical flow constraints. Concerning this, we propose a deep-learning based optical flow method designed to handle heavy rain. We introduce a feature multiplier…
Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the hi…
We present a joint Structure-Stereo optimization model that is robust for disparity estimation under low-light conditions. Eschewing the traditional denoising approach - which we show to be ineffective for stereo due to its artefacts and the questionable use of the PSNR metric, we propose to instead…
Optical flow estimation in rainy scenes is challenging due to degradation caused by rain streaks and rain accumulation, where the latter refers to the poor visibility of remote scenes due to intense rainfall. To resolve the problem, we introduce a residue channel, a single channel (gray) image that…
In this paper, we propose a method for separating known targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the known targets base…
The choice of motion models is vital in applications like image/video stitching and video stabilization. Conventional methods explored different approaches ranging from simple global parametric models to complex per-pixel optical flow. Mesh-based warping methods achieve a good balance between comput…
In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be e…
In this paper, a simultaneous sparsity representation-based binary hypothesis (S-SRBBH) model for target detection in hyperspectral image (HSI) is proposed. The S-SRBBH exploits the interpixel correlation within neighboring pixels in HSI, and then, each test pixel is represented by only the backgrou…
Estimating the number of clusters remains a difficult model selection problem. We consider this problem in the domain where the affinity relations involve groups of more than two nodes. Building on the previous formulation for the pairwise affinity case, we exploit the mathematical structures in the…
We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality. These descriptors are mainly trajectory-based, measuring sudden changes, temporal synchrony, and repetitiveness. The actionness map can be used to localize actions in a way that is gener…
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We firs…