Wenxiu Sun

Novel temporal domain hole filling based on background modeling for view synthesis

View synthesis is a technique to generate images/videos in a virtual viewpoint. In this paper, the dis-occlusion/hole problem in view synthesis is resolved from the temporal domain. By the fact that dis-occlusions belong to the background, firstly we build an online background under a newly designed Switchable Gaussian Model (SGM), owning to its computationally simplicity and scene adaptivity. Then, real textures in the dis-occlusions are able to be recovered with the built background. Experimental results have verified the improvements in rendering quality and computation complexity by comparing to the conventional spatial filling methods and other temporal filling methods.

Color clustering matting

Natural image matting refers to the problem of extracting regions of interest such as foreground object from an image based on user inputs like scribbles or trimap. More specifically, we need to estimate the color information of background, foreground and the corresponding opacity, which is an ill-posed problem inherently. Inspired by closed-form matting and KNN matting, in this paper, we extend the local color line model which is based on the assumption of linear color clustering within a small local window, to nonlocal feature space neighborhood. New affinity matrix is defined to achieve better clustering. Further, we demonstrate that good clustering ensures better prediction of alpha matte. Experimental evaluations on benchmark datasets and comparisons show that our matting algorithm is of higher accuracy and better visual quality than some state-of-the-art matting algorithms.

Rate-distortion optimized 3D reconstruction from noise-corrupted multiview depth videos

Transmitting compactly represented geometry of a dynamic scene from a sender can enable a multitude of 3D imaging functionalities at a receiver, such as synthesis of virtual images from freely chosen viewpoints via depth-image-based rendering (DIBR). While depth maps can now be readily captured using inexpensive depth sensors, they are often corrupted by non-negligible acquisition noise. In this paper, we derive 3D surfaces of a dynamic scene from noise-corrupted depth maps in a rate-distortion (RD) optimal manner. Specifically, unlike previous work that finds the most likely (e.g., maximum likelihood) 3D surface from noisy observations regardless of representation size, we judiciously search for the best fitting (i.e., minimum distortion) 3D surface subject to a bitrate constraint. Our RD-optimal solution reduces to the maximum likelihood solution as the rate constraint is loosened. Using the MVC codec for compression of multiview depth video and MPEG free viewpoint test sequences as input, experimental results show that RD-optimized 3D reconstructions computed by our algorithm outperform unprocessed depth maps by up to 2:42dB in PSNR of synthesized virtual views at the decoder for the same bitrate.

Rate-Constrained 3D Surface Estimation From Noise-Corrupted Multiview Depth Videos

Transmitting compactly represented geometry of a dynamic 3D scene from a sender can enable a multitude of imaging functionalities at a receiver, such as synthesis of virtual images at freely chosen viewpoints via depth-image-based rendering. While depth maps-projections of 3D geometry onto 2D image planes at chosen camera viewpoints-can nowadays be readily captured by inexpensive depth sensors, they are often corrupted by non-negligible acquisition noise. Given depth maps need to be denoised and compressed at the encoder for efficient network transmission to the decoder, in this paper, we consider the denoising and compression problems jointly, arguing that doing so will result in a better overall performance than the alternative of solving the two problems separately in two stages. Specifically, we formulate a rate-constrained estimation problem, where given a set of observed noise-corrupted depth maps, the most probable (maximum a posteriori (MAP)) 3D surface is sought within a search space of surfaces with representation size no larger than a prespecified rate constraint. Our rate-constrained MAP solution reduces to the conventional unconstrained MAP 3D surface reconstruction solution if the rate constraint is loose. To solve our posed rate-constrained estimation problem, we propose an iterative algorithm, where in each iteration the structure (object boundaries) and the texture (surfaces within the object boundaries) of the depth maps are optimized alternately. Using the MVC codec for compression of multiview depth video and MPEG free viewpoint video sequences as input, experimental results show that rate-constrained estimated 3D surfaces computed by our algorithm can reduce coding rate of depth maps by up to 32% compared with unconstrained estimated surfaces for the same quality of synthesized virtual views at the decoder.

Seamless View Synthesis Through Texture Optimization

In this paper, we present a novel view synthesis method named Visto, which uses a reference input view to generate synthesized views in nearby viewpoints. We formulate the problem as a joint optimization of inter-view texture and depth map similarity, a framework that is significantly different from other traditional approaches. As such, Visto tends to implicitly inherit the image characteristics from the reference view without the explicit use of image priors or texture modeling. Visto assumes that each patch is available in both the synthesized and reference views and thus can be applied to the common area between the two views but not the out-of-region area at the border of the synthesized view. Visto uses a Gauss-Seidel-like iterative approach to minimize the energy function. Simulation results suggest that Visto can generate seamless virtual views and outperform other state-of-the-art methods.

Error compensation and reliability based view synthesis

View synthesis offers a great flexibility in generating free viewpoint television (FTV) and 3D video (3DV). However, the depth-image-based view synthesis approach is very sensitive to errors in the camera parameters or poorly estimated depth maps (also called depth images). Because of these errors, three kinds of artifacts (blurring, contour, hole) are possibly introduced during the general synthesis process. Comparing to conventional methods which implement the view synthesis only in ideal case, in this paper, we propose to design an error compensation and reliability based view synthesis system where the potential errors are considered. The main contributions are highlighted as follows: Firstly, the camera parameter errors are compensated by a global homography transformation matrix. Secondly, the depth maps are classified into both reliable and unreliable regions and the reliability based weighting masks are built to blend synthesized images from two different views together. Finally, a reliability depth map based hole-filling technique is used to fill the existing holes. The experimental results demonstrate that these artifacts are efficiently reduced in the synthesized images.

Stereo matching by adaptive weighting selection based cost aggregation

Cost aggregation is the most essential step for dense stereo correspondence searching, which measures the similarity between pixels in the stereo images. In this paper, based on the analysis of the optimal adaptive weight, we propose a novel support aggregation strategy by adaptive weighting selection. The proposed method calculates the aggregation cost by the joint optimization of both left and right matching cost. By assigning more reasonable weighting coefficients, we exclude the occlusion pixels while preserving sufficient support region for accurate matching. The proposed optimal strategy can be integrated by any other adaptive weighting based cost aggregation method to generate more reasonable similarity measurement. Experimental results show that, compare with traditional methods, our algorithm can reduce the foreground fatten phenomenon while increasing the accuracy in the high texture regions.

Image rectification for single camera stereo system

Single camera stereo system utilizes mirrors and a single camera for computational stereo, where the mirrors provide extra views needed for stereo and 3D reconstruction. In this paper, we investigate the basic epiploar geometric properties of the single camera stereo image and propose a novel image rectification technique to map the epipolar lines in the original image into the horizontally aligned lines in the rectified image. Besides, the rotation angles of the single camera corresponding to the planar mirror are derived during rectification. Experimental results show the robustness and accuracy of our method.

A convex-optimization approach to dense stereo matching

We present a novel convex-optimization approach to solving the dense stereo matching problem in computer vision. Instead of directly solving for disparities of pixels, by establishing the connection between a permutation matrix and a disparity vector, we directly formulate the stereo matching problem as a continuous convex quadratic program in a simple, elegant and straightforward manner without performing any complicated relaxation or approximation. By using CVX, the Matlab software for disciplined convex programming, our method is extremely simple to implement.

A parallel deblocking filter based on H.264/AVC video coding standard

The deblocking filter in H.264/AVC is one of the most time consuming part of video decoder as its high content adaptation and data dependency lead to lots of computation. In this paper, we propose a novel parallel deblocking filter design based on the H.264/AVC video coding standard, taking the advantage that the data dependency of the deblocking filter are “periodic” in one dimension. Our proposed architecture successfully reduces the dependency between horizontal and vertical filters and utilizes the “periodic” property to achieve pixel-level parallelism. Algorithm analysis and experiment results on JM and GPU show that the proposed deblocking filter keeps as good a coding efficiency as that in H.264/AVC, and its high parallelism is suitable and promising in multi-core/multi-thread computing.