pwc-archive/papers-with-abstracts huggingface dataset:
Weighted Nuclear Norm Minimization with Application to Image Denoising
Authors: Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Image Denoising

As a convex relaxation of the low rank matrix factorization problem, the nuclear norm minimization has been attracting significant research interest in recent years. The standard nuclear norm minimization regularizes each singular value equally to pursue the convexity of the objective function. However, this greatly restricts its capability and flexibility in dealing with many practical problems (e.g., denoising), where the singular values have clear physical meanings and should be treated differently. In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights. The solutions of the WNNM problem are analyzed under different weighting conditions. We then apply the proposed WNNM algorithm to image denoising by exploiting the image nonlocal self-similarity. Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality.
Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation
Authors: Xing Zhang, Siwei Lyu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Kurtosis of 1D projections provides important statistical characteristics of natural images. In this work, we first provide a theoretical underpinning to a recently observed phenomenon known as projection kurtosis concentration that the kurtosis of natural images over different band-pass channels tend to concentrate around a typical value.  Based on this analysis, we further describe a new method to estimate the covariance matrix of correlated Gaussian noise from a noise corrupted image using random band-pass filters. We demonstrate the effectiveness of our blind noise covariance matrix estimation method on natural images.
Blind Image Quality Assessment using Semi-supervised Rectifier Networks
Authors: Huixuan Tang, Neel Joshi, Ashish Kapoor
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Blind Image Quality Assessment, Image Quality Assessment, Image Quality Estimation

It is often desirable to evaluate images quality with a perceptually relevant measure that does not require a reference image.  Recent approaches to this problem use human provided quality scores with machine learning to learn a measure. The biggest hurdles to these efforts are: 1) the difficulty of generalizing across diverse types of distortions and 2) collecting the enormity of human scored training data that is needed to learn the measure. We present a new blind image quality measure that addresses these difficulties by learning a robust, nonlinear kernel regression function using a rectifier neural network.  The method is pre-trained with unlabeled data and fine-tuned with labeled data.  It generalizes across a large set of images and distortion types without the need for a large amount of labeled data.  We evaluate our approach on two benchmark datasets and show that it not only outperforms the current state of the art in blind image quality estimation, but also outperforms the state of the art in non-blind measures.  Furthermore, we show that our semi-supervised approach is robust to using varying amounts of labeled data.
Separable Kernel for Image Deblurring
Authors: Lu Fang, Haifeng Liu, Feng Wu, Xiaoyan Sun, Houqiang Li
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring, Image Deblurring

In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system. Specifically, we decompose a blur kernel into three individual descriptors (trajectory, intensity and point spread function) so that they can be optimized separately. To demonstrate the advantages, we extract one-pixel-width trajectories of blur kernels and propose a random perturbation algorithm to optimize them but still keeping their continuity. For many cases, where current deblurring approaches fall into local minimum, excellent deblurred results and correct blur kernels can be obtained by individually optimizing the kernel trajectories. Our work strongly suggests that more constraints and priors should be introduced to blur kernels in solving the deblurring problem because blur kernels have lower dimensions than images.
Joint Depth Estimation and Camera Shake Removal from Single Blurry Image
Authors: Zhe Hu, Li Xu, Ming-Hsuan Yang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring, Depth Estimation, Image Matting

Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layer-based solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.
Deblurring Text Images via L0-Regularized Intensity and Gradient Prior
Authors: Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring, Image Deblurring, Image Restoration

We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.
Total Variation Blind Deconvolution: The Devil is in the Details
Authors: Daniele Perrone, Paolo Favaro
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.
Single Image Super-resolution using Deformable Patches
Authors: Yu Zhu, Yanning Zhang, Alan L. Yuille
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Super-Resolution, Patch Matching, Super-Resolution

We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.
Multi-Shot Imaging: Joint Alignment, Deblurring and Resolution-Enhancement
Authors: Haichao Zhang, Lawrence Carin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring, Image Restoration

The capture of multiple images is a simple way to increase the chance of capturing a good photo with a light-weight hand-held camera, for which the camera-shake blur is typically a nuisance problem. The naive approach of selecting the  single best  captured photo as output does not take full advantage of all the observations. Conventional multi-image blind deblurring methods can take all observations as input but  usually require  the multiple images are well aligned. However, the multiple blurry images captured in the presence of camera shake are rarely free from mis-alignment. Registering multiple blurry images is a challenging task due to the presence of blur while deblurring of multiple blurry images requires accurate alignment, leading to an intrinsically  coupled problem. In this paper, we propose  a blind multi-image restoration method which can achieve  joint alignment, non-uniform  deblurring, together with resolution enhancement from multiple low-quality images. Experiments on several real-world images with comparison to some previous methods validate the effectiveness of the proposed method.
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images
Authors: Huanjing Yue, Xiaoyan Sun, Jingyu Yang, Feng Wu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Image Denoising, Image Registration

In this paper, we propose a novel two-step scheme to filter heavy noise from images with the assistance of retrieved Web images. There are two key technical contributions in our scheme. First, for every noisy image block, we build two three dimensional (3D) data cubes by using similar blocks in retrieved Web images and similar nonlocal blocks within the noisy image, respectively. To better use their correlations, we propose different denoising strategies. The denoising in the 3D cube built upon the retrieved images is performed as median filtering in the spatial domain, whereas the denoising in the other 3D cube is performed in the frequency domain. These two denoising results are then combined in the frequency domain to produce a denoising image. Second, to handle heavy noise, we further propose using the denoising image to improve image registration of the retrieved Web images, 3D cube building, and the estimation of filtering parameters in the frequency domain. Afterwards, the proposed denoising is performed on the noisy image again to generate the final denoising result. Our experimental results show that when the noise is high, the proposed scheme is better than BM3D by more than 2 dB in PSNR and the visual quality improvement is clear to see.
Multipoint Filtering with Local Polynomial Approximation and Range Guidance
Authors: Xiao Tan, Changming Sun, Tuan D. Pham
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Depth Image Upsampling, Image Denoising

This paper presents a novel guided image filtering method using multipoint local polynomial approximation (LPA) with range guidance. In our method, the LPA is extended from a pointwise model into a multipoint model for reliable  filtering and better preserving image spatial variation which usually contains the essential information in the input image. In addition, we develop a scheme with constant computational complexity (invariant to the size of filtering kernel) for generating a spatial adaptive support region around a point. By using the hybrid of the local polynomial model and color/intensity based range guidance, the proposed method not only preserves edges but also does a much better job in preserving spatial variation than existing popular filtering methods. Our method proves to be effective in a number of applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Experimental results show that our method produces better results than state-of-the-art methods and it is also computationally efficient.
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising
Authors: Yi Peng, Deyu Meng, Zongben Xu, Chenqiang Gao, Yi Yang, Biao Zhang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Denoising, Dictionary Learning, Image Denoising

As compared to the conventional RGB or gray-scale images, multispectral images (MSI) can deliver more faithful representation for real scenes, and enhance the performance of many computer vision tasks. In practice, however, an MSI is always corrupted by various noises. In this paper we propose an effective MSI denoising approach by combinatorially considering two intrinsic characteristics underlying an MSI: the nonlocal similarity over space and the global correlation across spectrum. In specific, by explicitly considering spatial self-similarity of an MSI we construct a nonlocal tensor dictionary learning model with a group-block-sparsity constraint, which makes similar full-band patches (FBP) share the same atoms from the spatial and spectral dictionaries. Furthermore, through exploiting spectral correlation of an MSI and assuming over-redundancy of dictionaries, the constrained nonlocal MSI dictionary learning model can be decomposed into a series of unconstrained low-rank tensor approximation problems, which can be readily solved by off-the-shelf higher order statistics. Experimental results show that our method outperforms all state-of-the-art MSI denoising methods under comprehensive quantitative performance measures.
Robust 3D Features for Matching between Distorted Range Scans Captured by Moving Systems
Authors: Xiangqi Huang, Bo Zheng, Takeshi Masuda, Katsushi Ikeuchi
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

Laser range sensors are often demanded to mount on a moving platform for achieving the good efficiency of 3D reconstruction. However, such moving systems often suffer from the difficulty of matching the distorted range scans. In this paper, we propose novel 3D features which can be robustly extracted and matched even for the distorted 3D surface captured by a moving system. Our feature extraction employs Morse theory to construct Morse functions which capture the critical points approximately invariant to the 3D surface distortion. Then for each critical point, we extract support regions with the maximally stable region defined by extremal region or disconnectivity. Our feature description is designed as two steps: 1) we normalize the detected local regions to canonical shapes for robust matching; 2) we encode each key point with multiple vectors at different Morse function values. In experiments, we demonstrate that the proposed 3D features achieve substantially better performance for distorted surface matching than the state-of-the-art methods.
Discriminative Blur Detection Features
Authors: Jianping Shi, Li Xu, Jiaya Jia
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Deblurring

Ubiquitous image blur brings out a practically important question  what are effective features to differentiate between blurred and unblurred image regions. We address it by studying a few blur feature representations in image gradient, Fourier domain, and data-driven local filters. Unlike previous methods, which are often based on restoration mechanisms, our features are constructed to enhance discriminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth. Our results are applied to several applications, including blur region segmentation, deblurring, and blur magnification.
Detection, Rectification and Segmentation of Coplanar Repeated Patterns
Authors: James Pritts, Ondrej Chum, Jiri Matas
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

This paper presents a novel and general method for the detection, rectification and segmentation of imaged coplanar repeated patterns. The only assumption made of the scene geometry is that repeated scene elements are mapped to each other by planar Euclidean transformations. The class of patterns covered is broad and includes nearly all commonly seen, planar, man-made repeated patterns. In addition, novel linear constraints are used to reduce geometric ambiguity between the rectified imaged pattern and the scene pattern. Rectification to within a similarity of the scene plane is achieved from one rotated repeat, or to within a similarity with a scale ambiguity along the axis of symmetry from one reflected repeat. A stratum of constraints is derived that gives the necessary configuration of repeats for each successive level of rectification. A generative model for the imaged pattern is inferred and used to segment the pattern with pixel accuracy. Qualitative results are shown on a broad range of image types on which state-of-the-art methods fail.
Mirror Symmetry Histograms for Capturing Geometric Properties in Images
Authors: Marcelo Cicconet, Davi Geiger, Kristin C. Gunsalus, Michael Werman
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Symmetry Detection

We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients. We compute such a coefficient for every pair of pixels, and group them in a 6-dimensional histogram. By marginalizing the HMSC in various ways, we develop algorithms for a range of applications: detection of nearly-circular cells; location of the main axis of reflection symmetry; detection of cell-division in movies of developing embryos; detection of worm-tips and indirect cell-counting via supervised classification. Our approach generalizes a series of histogram-related methods, and the proposed algorithms perform with state-of-the-art accuracy.
A Learning-to-Rank Approach for Image Color Enhancement
Authors: Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Learning-To-Rank

We present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement.
Investigating Haze-relevant Features in A Learning Framework for Image Dehazing
Authors: Ketan Tang, Jianchao Yang, Jue Wang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Dehazing

Haze is one of the major factors that degrade outdoor images. Removing haze from a single image is known to be severely ill-posed, and assumptions made in previous methods do not hold in many situations. In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing. We show that the dark-channel feature is the most informative one for this task, which confirms the observation of He et al. [8] from a learning perspective, while other haze-relevant features also contribute significantly in a complementary way. We also find that surprisingly, the synthetic hazy image patches we use for feature investigation serve well as training data for realworld images, which allows us to train specific models for specific applications. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.
Quality Assessment for Comparing Image Enhancement Algorithms
Authors: Zhengying Chen, Tingting Jiang, Yonghong Tian
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Enhancement, Image Quality Assessment, NR-IQA

As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task. In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. Not like traditional image quality assessment approaches, we focus on the relative quality ranking between enhanced images rather than giving an absolute quality score for a single enhanced image. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images. A rank function is trained to fit the subjective assessment results, and can be used to predict ranks of new enhanced images which indicate the relative quality of enhancement algorithms. The experimental results show that our proposed approach statistically outperforms state-of-the-art general-purpose NR-IQA algorithms.