Jae Young Sim

Optimized contrast enhancement for real-time image and video dehazing

A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.

Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk With Restart

A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.

Multiscale Saliency Detection Using Random Walk With Restart

In this paper, we propose a graph-based multiscale saliency-detection algorithm by modeling eye movements as a random walk on a graph. The proposed algorithm first extracts intensity, color, and compactness features from an input image. It then constructs a fully connected graph by employing image blocks as the nodes. It assigns a high edge weight if the two connected nodes have dissimilar intensity and color features and if the ending node is more compact than the starting node. Then, the proposed algorithm computes the stationary distribution of the Markov chain on the graph as the saliency map. However, the performance of the saliency detection depends on the relative block size in an image. To provide a more reliable saliency map, we develop a coarse-to-fine refinement technique for multiscale saliency maps based on the random walk with restart (RWR). Specifically, we use the saliency map at a coarse scale as the restarting distribution of RWR at a fine scale. Experimental results demonstrate that the proposed algorithm detects visual saliency precisely and reliably. Moreover, the proposed algorithm can be efficiently used in the applications of proto-object extraction and image retargeting.

Single image dehazing based on contrast enhancement

A simple and adaptive single image dehazing algorithm is proposed in this work. Based on the observation that a hazy image has low contrast in general, we attempt to restore the original image by enhancing the contrast. First, the proposed algorithm estimates the airlight in a given hazy image based on the quad-tree subdivision. Then, the proposed algorithm estimates the transmission map to maximize the contrast of the output image. To measure the contrast, we develop a cost function, which consists of a standard deviation term and a histogram uniformness term. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct fine details in original scenes clearly.

Rate-distortion optimized compression and view-dependent transmission of 3-D normal meshes

A unified approach to rate-distortion (R-D) optimized compression and view-dependent transmission of three-dimensional (3-D) normal meshes is investigated in this work. A normal mesh is partitioned into several segments, which are then encoded independently. The bitstream of each segment is truncated optimally using a geometry distortion model based on the subdivision hierarchy. It is shown that the proposed compression algorithm yields a higher coding gain than the conventional algorithm. Moreover, to facilitate interactive transmission of 3-D data according to a clients viewing position, the server can allocate an adaptive bitrate to each segment based on its visibility priority. Simulation results demonstrate that the view-dependent transmission technique can reduce the bandwidth requirement considerably, while maintaining a good visual quality.

SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking

A simple yet effective object descriptor for visual tracking is proposed in this paper. We first decompose the bounding box of a target object into multiple patches, which are described by color and gradient histograms. Then, we concatenate the features of the spatially ordered patches to represent the object appearance. Moreover, to alleviate the impacts of background information possibly included in the bounding box, we determine patch weights using random walk with restart (RWR) simulations. The patch weights represent the importance of each patch in the description of foreground information, and are used to construct an object descriptor, called spatially ordered and weighted patch (SOWP) descriptor. We incorporate the proposed SOWP descriptor into the structured output tracking framework. Experimental results demonstrate that the proposed algorithm yields significantly better performance than the state-of-the-art trackers on a recent benchmark dataset, and also excels in another recent benchmark dataset.

Video Deraining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion

A novel algorithm to remove rain or snow streaks from a video sequence using temporal correlation and low-rank matrix completion is proposed in this paper. Based on the observation that rain streaks are too small and move too fast to affect the optical flow estimation between consecutive frames, we obtain an initial rain map by subtracting temporally warped frames from a current frame. Then, we decompose the initial rain map into basis vectors based on the sparse representation, and classify those basis vectors into rain streak ones and outliers with a support vector machine. We then refine the rain map by excluding the outliers. Finally, we remove the detected rain streaks by employing a low-rank matrix completion technique. Furthermore, we extend the proposed algorithm to stereo video deraining. Experimental results demonstrate that the proposed algorithm detects and removes rain or snow streaks efficiently, outperforming conventional algorithms.

Multiple random walkers and their application to image cosegmentation

A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work. In the MRW system, multiple agents traverse a single graph simultaneously. To achieve desired interactions among those agents, a restart rule can be designed, which determines the restart distribution of each agent according to the probability distributions of all agents. In particular, we develop the repulsive rule for data clustering. We illustrate that the MRW clustering can segment real images reliably. Furthermore, we propose a novel image cosegmentation algorithm based on the MRW clustering. Specifically, the proposed algorithm consists of two steps: inter-image concurrence computation and intra-image MRW clustering. Experimental results demonstrate that the proposed algorithm provides promising cosegmentation performance.

Single-image deraining using an adaptive nonlocal means filter

An adaptive rain streak removal algorithm for a single image is proposed in this work. We observe that a typical rain streak has an elongated elliptical shape with a vertical orientation. Thus, we first detect rain streak regions by analyzing the rotation angle and the aspect ratio of the elliptical kernel at each pixel location. We then perform the nonlocal means filtering on the detected rain streak regions by selecting nonlocal neighbor pixels and their weights adaptively. Experimental results demonstrate that the proposed algorithm removes rain streaks more efficiently and provides higher restored image qualities than conventional algorithms.

Consistent Stereo Matching Under Varying Radiometric Conditions

A consistent stereo matching (CSM) algorithm under varying radiometric conditions, such as lighting and exposure variations, for intermediate view synthesis is proposed in this work. First, we transform the colors of stereo images adaptively so that they are similar at corresponding pixels. Since the correspondences are generally unknown before stereo matching, we estimate pseudo-disparity vectors by sorting pixels based on the cumulative color histograms and use those pseudo vectors in the color transform. Then, to improve the accuracy of stereo matching, we jointly estimate the disparity maps for virtual intermediate views as well as those for real views, based on the consistency criterion that an object point should have the same disparity through all the views. Specifically, we compute matching costs using the reliability term and aggregate the costs to obtain initial disparity maps. We then refine the initial disparity maps by minimizing an energy function, which includes the consistency term. Experimental results show that the proposed CSM algorithm significantly reduces the error rate of disparity estimation under different radiometric conditions and synthesizes high quality intermediate views.