Junjun Jiang

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In this paper, we propose a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. We formulate this as a maximum-likelihood estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the expectation-maximization algorithm (EM), and the closed-form solutions of both rigid and nonrigid transformations are derived in the maximization step. In the nonrigid case, we model the transformation between images in a reproducing kernel Hilbert space (RKHS), and a sparse approximation is applied to the transformation that reduces the method computation complexity to linearithmic. Extensive experiments on real remote sensing images demonstrate accurate results of LLT, which outperforms current state-of-the-art methods, particularly in the case of severe outliers (even up to 80%).

Noise Robust Face Hallucination via Locality-Constrained Representation

Recently, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based models for face hallucination. In order to obtain the optimal weights of face hallucination, these approaches represent one image patch through other patches at the same position of training faces by employing least square estimation or sparse coding. However, they cannot provide unbiased approximations or satisfy rational priors, thus the obtained representation is not satisfactory. In this paper, we propose a simpler yet more effective scheme called Locality-constrained Representation (LcR). Compared with Least Square Representation (LSR) and Sparse Representation (SR), our scheme incorporates a locality constraint into the least square inversion problem to maintain locality and sparsity simultaneously. Our scheme is capable of capturing the non-linear manifold structure of image patch samples while exploiting the sparse property of the redundant data representation. Moreover, when the locality constraint is satisfied, face hallucination is robust to noise, a property that is desirable for video surveillance applications. A statistical analysis of the properties of LcR is given together with experimental results on some public face databases and surveillance images to show the superiority of our proposed scheme over state-of-the-art face hallucination approaches.

Face Super-Resolution via Multilayer Locality-Constrained Iterative Neighbor Embedding and Intermediate Dictionary Learning

Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm.

Position-Patch Based Face Hallucination via Locality-Constrained Representation

Instead of using probabilistic graph based or manifold learning based models, some approaches based on position-patch have been proposed for face hallucination recently. In order to obtain the optimal weights for face hallucination, they represent image patches through those patches at the same position of training face images by employing least square estimation or convex optimization. However, they can hope neither to provide unbiased solutions nor to satisfy locality conditions, thus the obtained patch representation is not the best. In this paper, a simpler but more effective representation scheme- Locality-constrained Representation (LcR) has been developed, compared with the Least Square Representation (LSR) and Sparse Representation (SR). It imposes a locality constraint onto the least square inversion problem to reach sparsity and locality simultaneously. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art face hallucination approaches.

Facial Image Hallucination Through Coupled-Layer Neighbor Embedding

As the facial image captured by a low-cost camera is typically very low resolution (LR), blurring, and noisy, traditional neighbor-embedding-based facial image hallucination methods from one single manifold (i.e., the LR image manifold) fail to reliably estimate the intention geometrical structure, consequently leading to a bias to the image reconstruction result. In this paper, we introduce the notion of neighbor embedding (NE) from the LR and the high-resolution (HR) image manifolds simultaneously and propose a novel NE model, termed the coupled-layer NE (CLNE), for facial image hallucination. CLNE differs substantially from other NE models in that it has two layers: the LR and the HR layers. The LR layer in this model is the local geometrical structure of the LR patch manifold, which is characterized by the reconstruction weights of the LR patches; the HR layer is the intrinsic geometry that can geometrically constrain the reconstruction weights. With this coupled-constraint paradigm between the adaptation of the LR layer and the HR one, CLNE can achieve a more robust NE through iteratively updating the LR patch reconstruction weights and the estimated HR patch. The experimental results in simulation and real conditions confirm that the proposed method outperforms the related state-of-the-art methods in both quantitative and visual comparisons.

Zero-Shot Person Re-identification via Cross-View Consistency

Person re-identification, aiming to identify images of the same person from various cameras configured in different places, has attracted much attention in the multimedia retrieval community. In this problem, choosing a proper distance metric is a crucial aspect, and many classic methods utilize a uniform learnt metric. However, their performance is limited due to ignoring the zero-shot and fine-grained characteristics presented in real person re-identification applications. In this paper, we investigate two consistencies across two cameras, which are cross-view support consistency and cross-view projection consistency. The philosophy behind it is that, in spite of visual changes in two images of the same person under two camera views, the support sets in their respective views are highly consistent, and after being projected to the same view, their context sets are also highly consistent. Based on the above phenomena, we propose a data-driven distance metric (DDDM) method, re-exploiting the training data to adjust the metric for each query-gallery pair. Experiments conducted on three public data sets have validated the effectiveness of the proposed method, with a significant improvement over three baseline metric learning methods. In particular, on the public VIPeR dataset, the proposed method achieves an accuracy rate of 42.09% at rank-1, which outperforms the state-of-the-art methods by 4.29%.

Face Hallucination Via Weighted Adaptive Sparse Regularization

Sparse representation-based face hallucination approaches proposed so far use fixed ℓ1 norm penalty to capture the sparse nature of face images, and thus hardly adapt readily to the statistical variability of underlying images. Additionally, they ignore the influence of spatial distances between the test image and training basis images on optimal reconstruction coefficients. Consequently, they cannot offer a satisfactory performance in practical face hallucination applications. In this paper, we propose a weighted adaptive sparse regularization (WASR) method to promote accuracy, stability and robustness for face hallucination reconstruction, in which a distance-inducing weighted ℓq norm penalty is imposed on the solution. With the adjustment to shrinkage parameter q , the weighted ℓq penalty function enables elastic description ability in the sparse domain, leading to more conservative sparsity in an ascending order of q . In particular, WASR with an optimal q > 1 can reasonably represent the less sparse nature of noisy images and thus remarkably boosts noise robust performance in face hallucination. Various experimental results on standard face database as well as real-world images show that our proposed method outperforms state-of-the-art methods in terms of both objective metrics and visual quality.

SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression With Local Structure Prior

The performance of traditional face recognition systems is sharply reduced when encountered with a low-resolution (LR) probe face image. To obtain much more detailed facial features, some face super-resolution (SR) methods have been proposed in the past decade. The basic idea of a face image SR is to generate a high-resolution (HR) face image from an LR one with the help of a set of training examples. It aims at transcending the limitations of optical imaging systems. In this paper, we regard face image SR as an image interpolation problem for domain-specific images. A missing intensity interpolation method based on smooth regression with a local structure prior (LSP), named SRLSP for short, is presented. In order to interpolate the missing intensities in a target HR image, we assume that face image patches at the same position share similar local structures, and use smooth regression to learn the relationship between LR pixels and missing HR pixels of one position patch. Performance comparison with the state-of-the-art SR algorithms on two public face databases and some real-world images shows the effectiveness of the proposed method for a face image SR in general. In addition, we conduct a face recognition experiment on the extended Yale-B face database based on the super-resolved HR faces. Experimental results clearly validate the advantages of our proposed SR method over the state-of-the-art SR methods in face recognition application.

Noise Robust Face Image Super-Resolution Through Smooth Sparse Representation

Face image super-resolution has attracted much attention in recent years. Many algorithms have been proposed. Among them, sparse representation (SR)-based face image super-resolution approaches are able to achieve competitive performance. However, these SR-based approaches only perform well under the condition that the input is noiseless or has small noise. When the input is corrupted by large noise, the reconstruction weights (or coefficients) of the input low-resolution (LR) patches using SR-based approaches will be seriously unstable, thus leading to poor reconstruction results. To this end, in this paper, we propose a novel SR-based face image super-resolution approach that incorporates smooth priors to enforce similar training patches having similar sparse coding coefficients. Specifically, we introduce the fused least absolute shrinkage and selection operator-based smooth constraint and locality-based smooth constraint to the least squares representation-based patch representation in order to obtain stable reconstruction weights, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FEI face database and CMU+MIT face database. Visual and quantitative comparisons show that the proposed face image super-resolution method yields superior reconstruction results when the input LR face image is contaminated by strong noise.

Sparse Support Regression for Image Super-Resolution

In most optical imaging systems and applications, images with high resolution (HR) are desired and often required. However, charged coupled device (CCD) and complementary metal-oxide semiconductor (CMOS) sensors may be not suitable for some imaging applications due to the current resolution level and consumer price. To transcend these limitations, in this paper, we present a novel single image super-resolution method. To simultaneously improve the resolution and perceptual image quality, we present a practical solution that combines manifold learning and sparse representation theory. The main contributions of this paper are twofold. First, a mapping function from low-resolution (LR) patches to HR patches will be learned by a local regression algorithm called sparse support regression, which can be constructed from the support bases of LR-HR dictionary. Second, we propose to preserve the geometrical structure of image patch dictionary, which is critical for reducing artifacts and obtaining better visual quality. Experimental results demonstrate that the proposed method produces high-quality results, both quantitatively and perceptually.