CNLPA-MVS:Coarse-Hypotheses Guided Non-Local PatchMatch Multi-View Stereo

In multi-view stereo,unreliable matching in low-textured regions has a negative impact on the completeness of reconstructed models.Since the photometric consistency of low-textured regions is not discriminative under a local window,non-local information provided by the Markov Random Field (MRF) model can alleviate the matching ambiguity but is limited in continuous space with high computational complexity.Owing to its sampling and propagation strategy,PatchMatch multi-view stereo methods have advantages in terms of optimizing the continuous labeling problem.In this paper,we propose a novel method to address this problem,namely the Coarse-Hypotheses Guided Non-Local PatchMatch Multi-View Stereo (CNLPA-MVS),which takes the advantages of both MRF-based non-local methods and PatchMatch multi-view stereo and compensates for their defects mutually.First,we combine dynamic programing (DP) and sequential propagation along scanlines in parallel to perform CNLPA-MVS,thereby obtaining the optimal depth and normal hypotheses.Second,we introduce coarse inference within a universal window provided by winner-takes-all to eliminate the stripe artifacts caused by DP and improve completeness.Third,we add a local consistency strategy based on the hypotheses of similar color pixels sharing approximate values into CNLPA-MVS for further improving completeness.CNLPA-MVS was validated on public benchmarks and achieved state-of-the-art performance with high completeness.     

ℒ0 Gradient‐Preserving Color Transfer

This paper presents a new two‐step color transfer method which includes color mapping and detail preservation. To map source colors to target colors, which are from an image or palette, the proposed similarity‐preserving color mapping algorithm uses the similarities between pixel color and dominant colors as existing algorithms and emphasizes the similarities between source image pixel colors. Detail preservation is performed by an ?0 gradient‐preserving algorithm. It relaxes the large gradients of the sparse pixels along color region boundaries and preserves the small gradients of pixels within color regions. The proposed method preserves source image color similarity and image details well. Extensive experiments demonstrate that the proposed approach has achieved a state‐of‐art visual performance.     

Distortion Optimization based Image Completion from a Large Displacement View

We present a new image completion method based on an additional large displacement view (LDV) of the same scene for faithfully repairing large missing regions on the target image in an automatic way. A coarse‐to‐fine distortion correction algorithm is proposed to minimize the perspective distortion in the corresponding parts for the common scene regions on the LDV image. First, under the assumption of a planar scene, the LDV image is warped according to a homography to generate the initial correction result. Second, the residual distortions in the common known scene regions are revealed by means of a mismatch detection mechanism and relaxed by energy optimization of overlap correspondences, with the expectations of color constancy and displacement field smoothness. The fundamental matrix for the two views is then computed based on the reliable correspondence set. Third, under the constraints of epipolar geometry, displacement field smoothness and color consistency of the neighboring pixels, the missing pixels are orderly restored according to a specially defined repairing priority function. We finally eliminate the ghost effect between the repaired region and its surroundings by Poisson image blending. Experimental results demonstrate that our method outperforms recent state‐of‐the‐art image completion methods for repairing large missing area with complex structure information.     

Optimum inpainting for depth map based on L 0 total variation

Depth map generated by the Kinect may have some pixels lost due to echo attenuation of infra- red light and mutual interference between neighboring pixels, which can cause pervasive problems when utilizing Kinect cameras as depth sensors. In this work, we propose a 2-step inpainting algorithm to infill the holes. First, a naive Bayesian estimation is conducted as preliminary inpainting scheme, utilizing neighboring pixels of the missing ones, and corresponding pixels in the color image as prior knowledge. After that, an optimization is implemented to improve the depth map, where the false edges in mistakenly inpainted regions are detected, then iteratively propelled to their true positions under total variation framework. Experimental results are included to show effectiveness of the proposed algorithm.     

Defocus Magnification

A blurry background due to shallow depth of field is often desired for photographs such as portraits, but, unfortunately, small point-and-shoot cameras do not permit enough defocus because of the small diameter of their lenses. We present an image-processing technique that increases the defocus in an image to simulate the shallow depth of field of a lens with a larger aperture. Our technique estimates the spatially-varying amount of blur over the image, and then uses a simple image-based technique to increase defocus. We first estimate the size of the blur kernel at edges and then propagate this defocus measure over the image. Using our defocus map, we magnify the existing blurriness, which means that we blur blurry regions and keep sharp regions sharp. In contrast to more difficult problems such as depth from defocus, we do not require precise depth estimation and do not need to disambiguate textureless regions.     

面向RGBD图像的标记分水岭分割

目的 针对分水岭分割算法中存在的过分割现象及现有基于RGB图像分割方法的局限,提出了一种基于RGB图像和深度图像(RGBD)的标记分水岭分割算法。方法 本文使用物体表面几何信息来辅助进行图像分割,定义了一种深度梯度算子和一种法向量梯度算子来衡量物体表面几何信息的变化。通过生成深度梯度图像和法向量梯度图像,与彩色梯度图像进行融合,实现标记图像的提取。在此基础上,使用极小值标定技术对彩色梯度图像进行修正,然后使用分水岭算法进行图像分割。结果 在纽约大学提供的NYU2数据集上进行实验,本文算法有效抑制了过分割现象,将分割区域从上千个降至数十个,且获得了与人工标定的分割结果更接近的分割效果,分割的准确率也比只使用彩色图像进行分割提高了10%以上。结论 本文算法普遍适用于RGBD图像的分割问题,该算法加入了物体表面几何信息的使用,提高了分割的准确率,且对颜色纹理相似的区域获得了较好的分割结果。     

An Improved Geometric Approach for Palette‐based Image Decomposition and Recoloring

Palette‐based image decomposition has attracted increasing attention in recent years. A specific class of approaches have been proposed basing on the RGB‐space geometry, which manage to construct convex hulls whose vertices act as palette colors. However, such palettes do not guarantee to have the representative colors which actually appear in the image, thus making it less intuitive and less predictable when editing palette colors to perform recoloring. Hence, we proposed an improved geometric approach to address this issue. We use a polyhedron, but not necessarily a convex hull, in the RGB space to represent the color palette. We then formulate the task of palette extraction as an optimization problem which could be solved in a few seconds. Our palette has a higher degree of representativeness and maintains a relatively similar level of accuracy compared with previous methods. For layer decomposition, we compute layer opacities via simple mean value coordinates, which could achieve instant feedbacks without precomputations. We have demonstrated our method for image recoloring on a variety of examples. In comparison with state‐of‐the‐art works, our approach is generally more intuitive and efficient with fewer artifacts.     

Bayesian Collaborative Denoising for Monte Carlo Rendering

The stochastic nature of Monte Carlo rendering algorithms inherently produces noisy images. Essentially, three approaches have been developed to solve this issue: improving the ray‐tracing strategies to reduce pixel variance, providing adaptive sampling by increasing the number of rays in regions needing so, and filtering the noisy image as a post‐process. Although the algorithms from the latter category introduce bias, they remain highly attractive as they quickly improve the visual quality of the images, are compatible with all sorts of rendering effects, have a low computational cost and, for some of them, avoid deep modifications of the rendering engine. In this paper, we build upon recent advances in both non‐local and collaborative filtering methods to propose a new efficient denoising operator for Monte Carlo rendering. Starting from the local statistics which emanate from the pixels sample distribution, we enrich the image with local covariance measures and introduce a nonlocal bayesian filter which is specifically designed to address the noise stemming from Monte Carlo rendering. The resulting algorithm only requires the rendering engine to provide for each pixel a histogram and a covariance matrix of its color samples. Compared to state‐of‐the‐art sample‐based methods, we obtain improved denoising results, especially in dark areas, with a large increase in speed and more robustness with respect to the main parameter of the algorithm. We provide a detailed mathematical exposition of our bayesian approach, discuss extensions to multiscale execution, adaptive sampling and animated scenes, and experimentally validate it on a collection of scenes.     

基于MRF的复杂图像抠图

所谓复杂图像抠图就是从复杂图像中抠取出目标物体的一种图像处理算法。为了取得更好的抠图效果,提出了一种基于马尔可夫随机场的自然图像抠图方法。该方法首先手工把图像分成3个区域:前景区域、背景区域和未知区域;然后,再将未知区域用手工粗略地划分成几个相交的小区域;接着在每一个小区域内,以其中的未知区域的像素点为节点,定义抠图标号,同时在这些节点上面建立MRF抠图模型,并把这些标号赋给这些节点,这样抠图问题被定义为在这个MRF模型和它的Gibbs分布上MAP估计问题;继而再计算出每个小区域的掩像;最后把这些掩像合并,即得到输入图像最终的掩像。和其他算法相比,对复杂图像的抠图问题,该方法可以取得更好的抠图效果。     

Gradient Outlier Removal for Gradient‐Domain Path Tracing

We present a new outlier removal technique for a gradient‐domain path tracing (G‐PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G‐PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.     

Two‐Colored Pixels

In this paper we show how to use two‐colored pixels as a generic tool for image processing. We apply two‐colored pixels as a basic operator as well as a supporting data structure for several image processing applications. Traditionally, images are represented by a regular grid of square pixels with one constant color each. In the two‐colored pixel representation, we reduce the image resolution and replace blocks of N × N pixels by one square that is split by a (feature) line into two regions with constant colors. We show how the conversion of standard mono‐colored pixel images into two‐colored pixel images can be computed efficiently by applying a hierarchical algorithm along with a CUDA‐based implementation. Two‐colored pixels overcome some of the limitations that classical pixel representations have, and their feature lines provide minimal geometric information about the underlying image region that can be effectively exploited for a number of applications. We show how to use two‐colored pixels as an interactive brush tool, achieving realtime performance for image abstraction and non‐photorealistic filtering. Additionally, we propose a realtime solution for image retargeting, defined as a linear minimization problem on a regular or even adaptive two‐colored pixel image. The concept of two‐colored pixels can be easily extended to a video volume, and we demonstrate this for the example of video retargeting.     

Unsupervised Dense Light Field Reconstruction with Occlusion Awareness

Light field (LF) reconstruction is a fundamental technique in light field imaging and has applications in both software and hardware aspects. This paper presents an unsupervised learning method for LF‐oriented view synthesis, which provides a simple solution for generating quality light fields from a sparse set of views. The method is built on disparity estimation and image warping. Specifically, we first use per‐view disparity as a geometry proxy to warp input views to novel views. Then we compensate the occlusion with a network by a forward‐backward warping process. Cycle‐consistency between different views are explored to enable unsupervised learning and accurate synthesis. The method overcomes the drawbacks of fully supervised learning methods that require large labeled training dataset and epipolar plane image based interpolation methods that do not make full use of geometry consistency in LFs. Experimental results demonstrate that the proposed method can generate high quality views for LF, which outperforms unsupervised approaches and is comparable to fully‐supervised approaches.     

Surface reflectance and normal estimation from photometric stereo

In this paper, we propose a new photometric stereo method for estimating diffuse reflection and surface normal from color images. Using dichromatic reflection model, we introduce surface chromaticity as a matching invariant for photometric stereo, which serves as the foundation of the theory of this paper. An extremely simple and robust reflection components separation method is proposed based on the invariant. Our separation method differs from most previous methods which either assume dependencies among pixels or require segmentation. We also show that a linear relationship between the image color and the surface normal can be obtained based on this invariant. The linear relationship turns the surface normal estimation problem into a linear system that can be solved exactly or via least-squares optimization. We present experiments on both synthetic and real images, which demonstrate the effectiveness of our method.     

Succinct Palette and Color Model Generation and Manipulation Using Hierarchical Representation

We propose a new method to obtain the representative colors and their distributions of an image. Our intuition is that it is possible to derive the global model from the local distributions. Beginning by sampling pure colors, we build a hierarchical representation of colors in the image via a bottom‐up approach. From the resulting hierarchy, we can obtain satisfactory palettes/color models automatically without a predefined size. Furthermore, we provide interactive operations to manipulate the results which allow the users to reflect their intention directly. In our experiment, we show that the proposed method produces more succinct results that faithfully represent all the colors in the image with an appropriate number of components. We also show that the proposed interactive approach can improve the results of applications such as recoloring and soft segmentation.     

Sub‐Pixel Anti‐Aliasing Via Triangle‐Based Geometry Reconstruction

Anti‐aliasing has recently been employed as a post‐processing step to adapt to the deferred shading technique in real‐time applications. Some of these existing algorithms store supersampling geometric information as geometric buffer (G‐buffer) to detect and alleviate sub‐pixel‐level aliasing artifacts. However, the anti‐aliasing filter based on sampled sub‐pixel geometries only may introduce unfaithful shading information to the sub‐pixel color in uniform‐geometry regions, and large G‐buffer will increase memory storage and fetch overheads. In this paper, we present a new Triangle‐based Geometry Anti‐Aliasing (TGAA) algorithm, to address these problems. The coverage triangle of each screen pixel is accessed, and then, the coverage information between the triangle and neighboring sub‐pixels is stored in a screen‐resolution bitmask, which allows the geometric information to be stored and accessed in an inexpensive manner. Using triangle‐based geometry, TGAA can exclude irrelevant neighboring shading samples and achieve faithful anti‐aliasing filtering. In addition, a morphological method of estimating the geometric edges in high‐frequency geometry is incorporated into the TGAA's anti‐aliasing filter to complement the algorithm. The implementation results demonstrate that the algorithm is efficient and scalable for generating high‐quality anti‐aliased images.     

基于双目视觉的人脸三维重建

基于双目立体匹配算法PatchMatch算法,提出了一种获取人脸三维点云的算法。该算法对局部立体匹配算法PatchMatch进行了优化。该方法既不需要昂贵的设备,也不需要通用的人脸三维模型,而是结合了人脸的拓扑结构信息以及立体视觉局部优化算法。此方法采用非接触式的双目视觉采集技术获取左右视角的人脸图像,利用回归树集合(ensemble of regression trees,ERT)算法对人脸图像进行关键点定位,恢复人脸稀疏的视差估计,运用线性插值方法初步估计脸部的稠密视差值,并结合局部立体匹配算法对得到的视差结果进行平滑处理,重建人脸的三维点云信息。实验结果表明,这种算法能够还原出光滑的稠密人脸三维点云信息,在人脸Bosphorus数据库上取得了更加准确的人脸重建结果。     

Overcoming shadows in 3-source photometric stereo

Light occlusions are one of the most significant difficulties of photometric stereo methods. When three or more images are available without occlusion, the local surface orientation is overdetermined so that shape can be computed and the shadowed pixels can be discarded. In this paper, we look at the challenging case when only two images are available without occlusion, leading to a one degree of freedom ambiguity per pixel in the local orientation. We show that, in the presence of noise, integrability alone cannot resolve this ambiguity and reconstruct the geometry in the shadowed regions. As the problem is ill-posed in the presence of noise, we describe two regularization schemes that improve the numerical performance of the algorithm while preserving the data. Finally, the paper describes how this theory applies in the framework of color photometric stereo where one is restricted to only three images and light occlusions are common. Experiments on synthetic and real image sequences are presented.     

Video color conceptualization using optimization

Color conceptualization aims to propagate"color concepts"from a library of natural color images to the input image by changing the main color.However,the existing method may lead to spatial discontinuities in images because of the absence of a spatial consistency constraint.In this paper,to solve this problem,we present a novel method to force neighboring pixels with similar intensities to have similar color.Using this constraint,the color conceptualization is formalized as an optimization problem with a quadratic cost function.Moreover,we further expand two-dimensional(still image)color conceptualization to three-dimensional(video),and use the information of neighboring pixels in both space and time to improve the consistency between neighboring frames.The performance of our proposed method is demonstrated for a variety of images and video sequences.     

Multi-frame stereo matching with edges,planes, and superpixels

We present a multi-frame narrow-baseline stereo matching algorithm based on extracting and matching edges across multiple frames. Edge matching allows us to focus on the important features at the very beginning, and deal with occlusion boundaries as well as untextured regions. Given the initial sparse matches, we fit overlapping local planes to form a coarse, over-complete representation of the scene. After breaking up the reference image in our sequence into superpixels, we perform a Markov random field optimization to assign each superpixel to one of the plane hypotheses. Finally, we refine our continuous depth map estimate using a piecewise-continuous variational optimization. Our approach successfully deals with depth discontinuities, occlusions, and large textureless regions, while also producing detailed and accurate depth maps. We show that our method out-performs competing methods on high-resolution multi-frame stereo benchmarks and is well-suited for view interpolation applications.     

A closed-form solution to natural image matting

Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed -- at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation.In this paper we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes for natural images may be obtained from a small amount of user input.