pwc-archive/papers-with-abstracts huggingface dataset:
Intrinsic Decomposition of Image Sequences From Local Temporal Variations
Authors: Pierre-Yves Laffont, Jean-Charles Bazin
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Intrinsic Image Decomposition

We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets.
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising
Authors: Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, Yi Ma
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Dictionary Learning, Image Denoising

Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising. However, two-dimensional low-rank model can not fully exploit the spatio-temporal correlation in larger data sets such as multispectral images and 3D MRIs. In this work, we propose a novel low-rank tensor approximation framework with Laplacian Scale Mixture (LSM)  modeling for multi-frame image denoising. First, similar 3D patches are grouped to form a tensor of d-order and high-order Singular Value Decomposition (HOSVD) is applied to the grouped tensor. Then the task of multiframe image denoising is formulated as a Maximum A Posterior (MAP) estimation problem with the LSM prior for tensor coefficients. Both unknown sparse coefficients and hidden LSM parameters can be efficiently estimated by the method of alternating optimization. Specifically, we have derived closed-form solutions for both subproblems. Experimental results on spectral and dynamic MRI images show that the proposed algorithm can better preserve the sharpness of important image structures and outperform several existing state-of-the-art multiframe denoising methods (e.g., BM4D and tensor dictionary learning).
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding
Authors: Yongbo Li, Weisheng Dong, Guangming Shi, Xuemei Xie
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval, Image Super-Resolution, Patch Matching, Retrieval, Super-Resolution

Existing approaches toward Image super-resolution (SR) is often either data-driven (e.g., based on internet-scale matching and web image retrieval) or model-based (e.g., formulated as an Maximizing a Posterior estimation problem). The former is conceptually simple yet heuristic; while the latter is constrained by the fundamental limit of frequency aliasing. In this paper, we propose to develop a hybrid approach toward SR by combining those two lines of ideas. More specifically, the parameters underlying sparse distributions of desirable HR image patches are learned from a pair of LR image and retrieved HR images. Our hybrid approach can be interpreted as the first attempt of reconciling the difference between parametric and nonparametric models for low-level vision tasks. Experimental results show that the proposed hybrid SR method performs much better than existing state-of-the-art methods in terms of both subjective and objective image qualities.
An Efficient Statistical Method for Image Noise Level Estimation
Authors: Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Noise Estimation

In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that many state-of-the-art noise estimation methods underestimate the noise level of an image. To this end, we derive a new nonparametric algorithm for efficient noise level estimation based on the observation that patches decomposed from a clean image often lie around a low-dimensional subspace. The performance of our method has been guaranteed both theoretically and empirically. Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments. We further demonstrate that the denoising algorithm BM3D algorithm achieves optimal performance using noise variance estimated by our algorithm.
Contour Detection and Characterization for Asynchronous Event Sensors
Authors: Francisco Barranco, Ching L. Teo, Cornelia Fermuller, Yiannis Aloimonos
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Boundary Detection, Contour Detection, General Classification, Segmentation

The bio-inspired, asynchronous event-based dynamic vision sensor records temporal changes in the luminance of the scene at high temporal resolution. Since events are only triggered at significant luminance changes, most events occur at the boundary of objects and their parts. The detection of these contours is an essential step for further interpretation of the scene. This paper presents an approach to learn the location of contours and their border ownership using Structured Random Forests on event-based features that encode motion, timing, texture, and spatial orientations. The classifier integrates elegantly information over time by utilizing the classification results previously computed. Finally, the contour detection and boundary assignment are demonstrated in a layer-segmentation of the scene. Experimental results demonstrate good performance in boundary detection and segmentation.
Class-Specific Image Deblurring
Authors: Saeed Anwar, Cong Phuoc Huynh, Fatih Porikli
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Deblurring, Image Deblurring

In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably. In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process. Specifically, we devise a prior based on the class-specific subspace of image intensity responses to band-pass filters. We learn that the aggregation of these subspaces across all frequency bands serves as a good class-specific prior for the restoration of frequencies that cannot be recovered with generic image priors. In an extensive validation, our method, equipped with the above prior, yields greater image quality than many state-of-the-art methods by up to 5 dB in terms of image PSNR, across various image categories including portraits, cars, cats, pedestrians and household objects.
Variational Depth Superresolution Using Example-Based Edge Representations
Authors: David Ferstl, Matthias Ruther, Horst Bischof
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images as guidance for superresolution. In our method we learn a dictionary of edge priors from an external database of high and low resolution examples. In a novel variational sparse coding approach this dictionary is used to infer strong edge priors. Additionally to the traditional sparse coding constraints the difference in the overlap of neighboring edge patches is minimized in our optimization. These edge priors are used in a novel variational superresolution as anisotropic guidance of the higher order regularization. Both the sparse coding and the variational superresolution of the depth are solved based on a primal-dual formulation. In an exhaustive numerical and visual evaluation we show that our method clearly outperforms existing approaches on multiple real and synthetic datasets.
Conditioned Regression Models for Non-Blind Single Image Super-Resolution
Authors: Gernot Riegler, Samuel Schulter, Matthias Ruther, Horst Bischof
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Super-Resolution, regression, Super-Resolution

Single image super-resolution is an important task in the field of computer vision and finds many practical applications.  Current state-of-the-art methods typically rely on machine learning algorithms to infer a mapping from low- to high-resolution images.  These methods use a single fixed blur kernel during training and, consequently, assume the exact same kernel underlying the image formation process for all test images.  However, this setting is not realistic for practical applications, because the blur is typically different for each test image.  In this paper, we loosen this restrictive constraint and propose conditioned regression models (including convolutional neural networks and random forests) that can effectively exploit the additional kernel information during both, training and inference.  This allows for training a single model, while previous methods need to be re-trained for every blur kernel individually to achieve good results, which we demonstrate in our evaluations.  We also empirically show that the proposed conditioned regression models (i) can effectively handle scenarios where the blur kernel is different for each image and (ii) outperform related approaches trained for only a single kernel.
Video Super-Resolution via Deep Draft-Ensemble Learning
Authors: Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Ensemble Learning, Image Deconvolution, Super-Resolution, Video Super-Resolution

We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution. Our method contains two main components -- i.e., SR draft ensemble generation and its optimal reconstruction. The first component is to renovate traditional feedforward reconstruction pipeline and greatly enhance its ability to compute different super resolution results considering large motion variation and possible errors arising in this process. Then we combine SR drafts through the nonlinear process in a deep convolutional neural network (CNN). We analyze why this framework is proposed and explain its unique advantages compared to previous iterative methods to update different modules in passes. Promising experimental results are shown on natural video sequences.
Pan-Sharpening With a Hyper-Laplacian Penalty
Authors: Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John Paisley
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Computational Efficiency

Pan-sharpening is the task of fusing spectral information in low resolution multispectral images with spatial information in a corresponding high resolution panchromatic image. In such approaches, there is a trade-off between spectral and spatial quality, as well as computational efficiency. We present a method for pan-sharpening in which a sparsity-promoting objective function preserves both spatial and spectral content, and is efficient to optimize. Our objective incorporates the L1/2-norm in a way that can leverage recent computationally efficient methods, and L1 for which the alternating direction method of multipliers can be used. Additionally, our objective penalizes image gradients to enforce high resolution fidelity, and exploits the Fourier domain for further computational efficiency. Visual quality metrics demonstrate that our proposed objective function can achieve higher spatial and spectral resolution than several previous well-known methods with competitive computational efficiency.
Video Restoration Against Yin-Yang Phasing
Authors: Xiaolin Wu, Zhenhao Li, Xiaowei Deng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Tone Mapping, Video Restoration

A common video degradation problem, which is largely untreated in literature, is what we call Yin-Yang Phasing (YYP).  YYP is characterized by involuntary, dramatic flip-flop in the intensity and possibly chromaticity of an object as the video plays.  Such temporal artifacts occur under ill illumination conditions and are triggered by object or/and camera motions, which mislead the settings of camera's auto-exposure and white point.  In this paper, we investigate the problem and propose a video restoration technique to suppress YYP artifacts and retain temporal consistency of objects appearance via inter-frame, spatially-adaptive, optimal tone mapping. The video quality can be further improved by a novel image enhancer designed in Weber's perception principle and by exploiting the second-order statistics of the scene.  Experimental results are encouraging, pointing to an effective, practical solution for a common but surprisingly understudied problem.
Rolling Shutter Super-Resolution
Authors: Abhijith Punnappurath, Vijay Rengarajan, A. N. Rajagopalan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Super-Resolution, Super-Resolution

Classical multi-image super-resolution (SR) algorithms, designed for CCD cameras, assume that the motion among the images is global. But CMOS sensors that have increasingly started to replace their more expensive CCD counterparts in many applications do not respect this assumption if there is a motion of the camera relative to the scene during the exposure duration of an image because of the row-wise acquisition mechanism. In this paper, we study the hitherto unexplored topic of multi-image SR in CMOS cameras. We initially develop an SR observation model that accounts for the row-wise distortions called the ``rolling shutter'' (RS) effect observed in images captured using non-stationary CMOS cameras. We then propose a unified RS-SR framework to obtain an RS-free high-resolution image (and the row-wise motion) from distorted low-resolution images. We demonstrate the efficacy of the proposed scheme using synthetic data as well as real images captured using a hand-held CMOS camera. Quantitative and qualitative assessments reveal that our method significantly advances the state-of-the-art.
Learning Large-Scale Automatic Image Colorization
Authors: Aditya Deshpande, Jason Rock, David Forsyth
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Colorization, Image Colorization

We describe an automated method for image colorization that learns to colorize from examples.  Our method exploits a LEARCH framework to train a quadratic objective function in the chromaticity maps, comparable to a Gaussian random field.  The coefficients of the objective function are conditioned on image features, using a random forest.  The objective function admits correlations on long spatial scales, and can control spatial error in the colorization of the image.  Images are then colorized by minimizing this objective function.  We demonstrate that our method strongly outperforms a natural baseline on large-scale experiments with images of real scenes using a demanding loss function.  We demonstrate that learning a model that is conditioned on scene produces improved results. We show how to incorporate a desired color histogram into the objective function, and that doing so can lead to further improvements in results.
Multiple-Hypothesis Affine Region Estimation With Anisotropic LoG Filters
Authors: Takahiro Hasegawa, Mitsuru Ambai, Kohta Ishikawa, Gou Koutaki, Yuji Yamauchi, Takayoshi Yamashita, Hironobu Fujiyoshi
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter. Although conventional affine region detectors, such as Hessian/Harris-Affine, iterate to find an affine region that fits a given image patch, such iterative searching is adversely affected by an initial point. To avoid this problem, we allow multiple detections from a single keypoint. We demonstrate that the responses of all possible anisotropic LoG filters can be efficiently computed by factorizing them in a similar manner to spectral SIFT. A large number of LoG filters that are densely sampled in a parameter space are reconstructed by a weighted combination of a limited number of representative filters, called ``eigenfilters", by using singular value decomposition. Also, the reconstructed filter responses of the sampled parameters can be interpolated to a continuous representation by using a series of proper functions. This results in efficient multiple extrema searching in a continuous space. Experiments revealed that our method has higher repeatability than the conventional methods.
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection
Authors: Dingwen Zhang, Deyu Meng, Chao Li, Lu Jiang, Qian Zhao, Junwei Han
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Co-Salient Object Detection, Multiple Instance Learning, Saliency Detection, Self-Learning

As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects in a group of images. Traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to explore the intrinsic patterns underlying co-salient objects. Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications, especially due to their lack of insightful understanding of the biological mechanisms of human visual co-attention. To alleviate this problem, we propose a novel framework for this task, by naturally reformulating it as a multiple-instance learning (MIL) problem and further integrating it into a self-paced learning (SPL) regime. The proposed framework on one hand is capable of fitting insightful metric measurements and discovering common patterns under co-salient regions in a self-learning way by MIL, and on the other hand tends to promise the learning reliability and stability by simulating the human learning process through SPL. Experiments on benchmark datasets have demonstrated the effectiveness of the proposed framework as compared with the state-of-the-arts.
External Patch Prior Guided Internal Clustering for Image Denoising
Authors: Fei Chen, Lei Zhang, Huimin Yu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Denoising, Image Denoising

Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive denoising results, the improvement of visual quality will become very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models (GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisypatches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM.
Self-Calibration of Optical Lenses
Authors: Michael Hirsch, Bernhard Scholkopf
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: BIG-bench Machine Learning

Even high-quality lenses suffer from optical aberrations, especially when used at full aperture. Furthermore, there are significant lens-to-lens deviations due to manufacturing tolerances, often rendering current software solutions like DxO, Lightroom, and PTLens insufficient as they don't adapt and only include generic lens blur models.  We propose a method that enables the self-calibration of lenses from a natural image, or a set of such images. To this end we develop a machine learning framework that is able to exploit several recorded images and distills the available information into an accurate model of the considered lens.
Illumination Robust Color Naming via Label Propagation
Authors: Yuanliu liu, Zejian yuan, Badong Chen, Jianru Xue, Nanning Zheng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Retrieval, Retrieval

Color composition is an important property for many computer vision tasks like image retrieval and object classification. In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically. We achieve this through color label propagation without recovering the intrinsic reflectance beforehand. Specifically, the color labels are propagated between regions sharing the same reflectance, and the direction of propagation is promoted to be from regions under full illumination and normal view angles to abnormal regions. We detect shadowed and highlighted regions as well as pairs of regions that have similar reflectance. A joint inference process is adopted to trim the inconsistent identities and connections. For evaluation we collect three datasets of images under noticeable highlights and shadows. Experimental results show that our model can effectively describe the color composition of real-world images.
Unsupervised Cross-Modal Synthesis of Subject-Specific Scans
Authors: Raviteja Vemulapalli, Hien Van Nguyen, Shaohua Kevin Zhou
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Generation

Recently, cross-modal synthesis of subject-specific scans has been receiving significant attention from the medical imaging community. Though various synthesis approaches have been introduced in the recent past, most of them are either tailored to a specific application or proposed for the supervised setting, i.e., they assume the availability of training data from the same set of subjects in both source and target modalities. But, collecting multiple scans from each subject is undesirable. Hence, to address this issue, we propose a general unsupervised cross-modal medical image synthesis approach that works without paired training data. Given a source modality image of a subject, we first generate multiple target modality candidate values for each voxel independently using cross-modal nearest neighbor search. Then, we select the best candidate values jointly for all the voxels by simultaneously maximizing a global mutual information cost function and a local spatial consistency cost function. Finally, we use coupled sparse representation for further refinement of synthesized images. Our experiments on generating T1-MRI brain scans from T2-MRI and vice versa demonstrate that the synthesis capability of the proposed unsupervised approach is comparable to various state-of-the-art supervised approaches in the literature.