Showing papers by "Guangming Shi published in 2014"

Compressive Sensing via Nonlocal Low-Rank Regularization

Abstract: Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det ( X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.

Image Quality Assessment with Degradation on Spatial Structure

Abstract: In this letter, we introduce an improved structural degradation based image quality assessment (IQA) method. Most of the existing structural similarity based IQA metrics mainly consider the spatial contrast degradation but have not fully considered the changes on the spatial distribution of structures. Since the human visual system (HVS) is sensitive to degradations on both spatial contrast and spatial distribution, both factors need to be considered for IQA. In order to measure the structural degradation on spatial distribution, the local binary patterns (LBPs) are first employed to extract structural information. And then, the LBP shift between the reference and distorted images is computed, because noise distorts structural patterns. Finally, the spatial contrast degradation on each pair of LBP shifts is calculated for quality assessment. Experimental results on three large benchmark databases confirm that the proposed IQA method is highly consistent with the subjective perception.

Nonlocal sparse and low-rank regularization for optical flow estimation.

Abstract: Designing an appropriate regularizer is of great importance for accurate optical flow estimation. Recent works exploiting the nonlocal similarity and the sparsity of the motion field have led to promising flow estimation results. In this paper, we propose to unify these two powerful priors. To this end, we propose an effective flow regularization technique based on joint low-rank and sparse matrix recovery. By grouping similar flow patches into clusters, we effectively regularize the motion field by decomposing each set of similar flow patches into a low-rank component and a sparse component. For better enforcing the low-rank property, instead of using the convex nuclear norm, we use the log det (·) function as the surrogate of rank, which can also be efficiently minimized by iterative singular value thresholding. Experimental results on the Middlebury benchmark show that the performance of the proposed nonlocal sparse and low-rank regularization method is higher than (or comparable to) those of previous approaches that harness these same priors, and is competitive to current state-of-the-art methods.

Convex Combination Based Target Localization with Noisy Angle of Arrival Measurements

Abstract: Target localization based on angle of arrival (AoA) is an important branch of location estimation research. Due to the noisy measurements from sensors and the non-linear inverse trigonometry function, the AoA-based localization induces a non-convex optimization that is difficult to solve with both high speed and accuracy simultaneously. To achieve a fast and accurate algorithm, we propose a convex combination scheme. By introducing a highly accurate linear approximation to the inverse trigonometric function, the objective is converted to a convex function, which can be solved efficiently with linear least-squares approach. The key point of the convex combination scheme is to express the coordinate of the target as the convex combination of a set of virtual anchors around its real position. Simulations demonstrate that the accuracy of this method is very close to the Cramer-Rao lower bound and that the comprehensive performance, including the accuracy and speed, is significantly improved compared to other state-of-the-art methods.

Reduced-reference image quality assessment with local binary structural pattern

Abstract: Reduced-reference (RR) image quality assessment (IQA) aims to use less reference data and achieve higher quality prediction accuracy. Recent researches confirm that the human visual system (HVS) is adapted to extract structural information and is sensitive to structure degradation. Therefore, in this paper, we try to represent image contents with several structural patterns, and measure image quality according to the structural degradation on these patterns. The classic local binary patterns (LBPs) are firstly employed to extract image structures and create LBP based structural histogram. And then, the structural degradation is computed as the histogram distance between the reference and distorted images. Experimental results on three large databases demonstrate that the proposed RR IQA method greatly improved the quality prediction accuracy.

Sparsity Fine Tuning in Wavelet Domain With Application to Compressive Image Reconstruction

Abstract: In compressive sensing, wavelet space is widely used to generate sparse signal (image signal in particular) representations. In this paper, we propose a novel approach of statistical context modeling to increase the level of sparsity of wavelet image representations. It is shown, contrary to a widely held assumption, that high-frequency wavelet coefficients have nonzero mean distributions if conditioned on local image structures. Removing this bias can make wavelet image representations sparser, i.e., having a greater number of zero and close-to-zero coefficients. The resulting unbiased probability models can significantly improve the performance of existing wavelet-based compressive image reconstruction methods in both PSNR and visual quality. An efficient algorithm is presented to solve the compressive image recovery (CIR) problem using the refined models. Experimental results on both simulated compressive sensing (CS) image data and real CS image data show that the new CIR method significantly outperforms existing CIR methods in both PSNR and visual quality.

Image enhancement by entropy maximization and quantization resolution upconversion

Abstract: This article introduces a new contrast enhancement algorithm of tone-preserving entropy maximization Its design objective is to present the maximal amount of information content in the enhanced image, or being optimal in an information theoretical sense, while preventing the loss of tone continuity The resulting optimization problem can be graph-theoretically modeled as the construction of K-edge maximum-weight path, and it can be solved efficiently by dynamic programming Moreover, the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog-to-digital conversion of image signals Empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm

Dual-channel multispectral video imaging device and imaging method based on compressive sensing

Abstract: A compressive sensing-based multispectral video imager comprises a beamsplitter, a first light channel, a second light channel, and an image reconstruction processor; the beamsplitter is configured to divide the beam of an underlying image into a first light beam and a second light beam; the first light beam enters the first light channel, processed and sampled in the first light channel, to obtain a first mixing spectral image which is transferred to the image reconstruction processor; the second light beam enters the second light channel, processed and sampled in the second light channel, to obtain a second mixing spectral image which is transferred to the image reconstruction processor; the image reconstruction processor is configured to reconstruct the underlying spectral image based on the first mixing spectral image and the second mixing spectral image by using non-linear optimization method.

Method for acquiring three-dimensional information of frequency mixing structured light based on phase encoding

Abstract: The invention discloses a method for acquiring three-dimensional information of frequency mixing structured light based on phase encoding, mainly solving the problems of low measuring precision, low spatial resolution and long consumed time of the traditional three-dimensional information acquiring method. The method is realized through the steps: designing a double-color stripe template with two kinds of frequency information and changed strength; projecting the double-color stripe template on an objected to be measured by using a projector, and recording a deformed stripe image by using a camera; solving the color and strength phase distribution of the deformed stripe image; calculating truncation phase expansion values of pixel points in the deformed stripe image according to the color and strength phase distribution; confirming matching points of the pixel points in the projected template according to the truncation phase expansion values; and solving the three-dimensional coordinate value of each pixel point in the deformed stripe image according to a triangulation theory and the coordinates of the matching points. The method has the advantages of high space resolution, high measuring precision and high measuring speed and can be used for acquiring the three-dimensional information of dynamic objects.

Sparsity fine tuning in wavelet domain with application to compressive image reconstruction

Abstract: In compressive sensing, wavelet space is widely used to generate sparse signal (image signal in particular) representations. In this paper, we propose a novel approach of statistical context modeling to increase the level of sparsity of wavelet image representations. It is shown, contrary to a widely held assumption, that high-frequency wavelet coefficients have nonzero mean distributions if conditioned on local image structures. Removing this bias can make wavelet image representations sparser, i.e., having a greater number of zero and close-to-zero coefficients. The resulting unbiased probability models can significantly improve the performance of existing wavelet-based compressive image reconstruction methods in both PSNR and visual quality. An efficient algorithm is presented to solve the compressive image recovery (CIR) problem using the refined models. Experimental results on both simulated compressive sensing (CS) image data and real CS image data show that the new CIR method significantly outperforms existing CIR methods in both PSNR and visual quality.

Image restoration via Bayesian structured sparse coding

Abstract: In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) − if treated as a latent variable − can be jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR.

Target object three-dimensional information acquisition method based on periodical co-prime hybrid coding

Abstract: The invention discloses a target object three-dimensional information acquisition method based on periodical co-prime hybrid coding. The target object three-dimensional information acquisition method mainly solves the problem that according to an existing phase shifting method, ambiguity exists in the prolongation process of a truncated phase position. The target object three-dimensional information acquisition method comprises the implementation steps that firstly, three template images T1, T2 and T3 are designed; secondly, a projector P and a video camera V are horizontally placed, the optical axis of the projector P and the optical axis of the video camera V are parallel to each other, and the three template images T1, T2 and T3 are sequentially projected onto a target object through the projector P; thirdly, corresponding deformation images (delta1), (delta2) and (delta3) generated after the three template images are projected onto the target object are shot synchronously through the video camera V, the deformation images are transmitted back to a computer and are decoded, so that a sine stripe truncated phase position phi ps and a square wave stripe truncated phase position phi sq of each deformation image are obtained; fourthly, according to the sine stripe truncated phase position phi ps and a square wave stripe truncated phase position phi sq of each deformation image, three-dimensional information of the target object is obtained through solution. The target object three-dimensional information acquisition method based on periodical co-prime hybrid coding has the advantages that the anti-jamming capability is high, the target object three-dimensional information measurement accuracy is high and the resolution ratio is high, and therefore the target object three-dimensional information acquisition method can be applied to the fields of reverse engineering, man-machine interaction and cultural relic three-dimensional reconstruction and the like.

One-shot depth acquisition with a random binary pattern.

Abstract: In this paper, we propose a new spatial encoding method that integrates the random binary pattern and the improved phase-difference-matching method to acquire a dense and precise depth map. The adopted binary pattern can simplify pattern projecting devices compared with the patterns that use color. The density of speckles in the pattern is periodic and the positions of them are random. Based on these two properties, we propose an improved phase-difference corresponding method, which is divided into two steps: the coarse matching step to estimate the approximate coordinates of pixels in the pattern via analyzing the phase values of the image, and the fine matching step to compensate errors of the coarse matching results and to achieve subpixel accuracy. This matching method does not require an extra optimization method with high computational complexity. In the experiment, we show the effectiveness of the proposed method. We also evaluate this method in actual experiments. The results show that this method has advantages over the time-of-flight camera and Kinect, particularly in terms of precision.

Depth recovery with face priors

Abstract: Existing depth recovery methods for commodity RGB-D sensors primarily rely on low-level information for repairing the measured depth estimates. However, as the distance of the scene from the camera increases, the recovered depth estimates become increasingly unreliable. The human face is often a primary subject in the captured RGB-D data in applications such as the video conference. In this paper we propose to incorporate face priors extracted from a general sparse 3D face model into the depth recovery process. In particular, we propose a joint optimization framework that consists of two main steps: deforming the face model for better alignment and applying face priors for improved depth recovery. The two main steps are iteratively and alternatively operated so as to help each other. Evaluations on benchmark datasets demonstrate that the proposed method with face priors significantly outperforms the baseline method that does not use face priors, with up to 15.1 % improvement in depth recovery quality and up to 22.3 % in registration accuracy.

A Unitary ESPRIT Scheme of Joint Angle Estimation for MOTS MIMO Radar

Abstract: The transmit array of multi-overlapped-transmit-subarray configured bistatic multiple-input multiple-output (MOTS MIMO) radar is partitioned into a number of overlapped subarrays, which is different from the traditional bistatic MIMO radar. In this paper, a new unitary ESPRIT scheme for joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for MOTS MIMO radar is proposed. In our method, each overlapped-transmit-subarray (OTS) with the identical effective aperture is regarded as a transmit element and the characteristics that the phase delays between the two OTSs is utilized. First, the measurements corresponding to all the OTSs are partitioned into two groups which have a rotational invariance relationship with each other. Then, the properties of centro-Hermitian matrices and real-valued rotational invariance factors are exploited to double the measurement samples and reduce computational complexity. Finally, the close-formed solution of automatically paired DOAs and DODs of targets is derived in a new manner. The proposed scheme provides increased estimation accuracy with the combination of inherent advantages of MOTS MIMO radar with unitary ESPRIT. Simulation results are presented to demonstrate the effectiveness and advantage of the proposed scheme.

Method for obtaining depth of structured light dynamic scene on basis of random templates

Abstract: The invention discloses a method for obtaining the depth of a structured light dynamic scene on the basis of random templates. The method mainly solves the problems that the prior art is low in depth spatial resolution and depth precision. The method comprises the implementation steps of designing the random templates P, projecting the random templates P into a three-dimensional scene through a projector T, and recording deformed images U which are modulated through the scene by using a video camera C; averaging the random templates P and deformed image U sliding windows to obtain stripe-liked images Q and deformed stripe images B; solving the phase positions of pixels in the stripe-liked images Q and in the deformed stripe images B by using a Gabor filter; obtaining a rough matching result of the pixels in the deformed stripe images B and the pixels in the stripe-liked images Q through a phase relation; carrying out compensation on the rough matching result to obtain an ultimate accurate matching result; solving the depth of the corresponding scene by utilizing coordinates of matching points and a line and plane intersection geometrical relationship. The method for obtaining the depth of the structured light dynamic scene on the basis of the random template has the advantages of being high in depth spatial resolution and low in calculation complexity, and is capable of being applied to accurate three-dimensional reconstruction of the dynamic scene.

Structural uncertainty based just noticeable difference estimation.

Abstract: Just noticeable difference (JND) reveals the minimum visible threshold of the human visual system (HVS), which is useful in visual redundancy reduction. Existing JND models estimate the visible threshold with luminance adaptation and contrast masking. As a result, the smooth and edge regions are effectively estimated, while the disorderly texture regions are always underestimated. The disorderly texture regions possess a large amount of disorderly structures and the HVS cannot fully perceive them. Therefore, in this work, we suggest to consider the disorder degree of structure for JND threshold estimation. According to the correlation among neighboring pixels, the uncertain information is extracted, and the disorder degree of structure is computed, which we called structural uncertainty. Then, taking the effect of background luminance, contrast, and structural uncertainty into account, a novel JND model is deduced. Experimental results demonstrate that the proposed JND can accurately estimate the visible thresholds of different image regions. Moreover, the proposed JND is adopted to remove visual redundancy for JPEG compression, which saves about 14% bit rate while keeping the perceptual quality.

A flexible design for coded aperture snapshot spectral imager

Abstract: By the success of compressive sensing (CS), coded aperture snapshot spectral imager (CASSI) computationally obtains 3D spectral images from 2D compressive measurement. In CASSI, each pixel of the detector captures spectral information only from one voxel in each band with binary weights (i.e., 0 or 1), which limits the variety of superposition relationship among the 3D voxels in the underlying scene. Moreover, the correspondence of each pixel of detector to each pixel of coded aperture cannot be readily achieved in the presence of dispersive prism, due to the small pixel sizes of these elements (often in micrometer). In this paper, we propose a flexible design to improve the performance of CASSI with currently employed optical elements in CASSI. Specifically, the proposed design integrates a kind of flexible alignment relationship along the coded aperture, the dispersive prism and the detector. Each measurement of the detector is manifested as the summation of several voxels in each band with random decimal weights and different measurements corresponds to overlapped voxels, which provides more sufficient superposition relationship of the scene information. This flexible design favors the sensing mechanism better satisfy the requirement of CS theory. Furthermore, the proposed design greatly reduces the alignment complexity and burden of system construction. Preliminary result achieves improved image quality, including higher PSNR and better perceptual effect, compared to the traditional design.

SAR Image Despeckling Based on the Nonlocally Centralized Sparse Representation Model

Abstract: In this paper, a novel SAR image despeckling method based on the nonlocal centralized sparse representation (NCSR) is presented. NCSR model is initially proposed for image restoration, exhibiting excellent denoising performance for nature images corrupted by additive Gaussian noise. However, SAR images, whose speckle noise is multiplicative and follows non-Gaussian distribution, are very different from the nature images. Considering the properties of speckle, SAR images are pre-processed, and then based on the generalized likelihood ratio (GLR) criteria, a new metric function is developed for acquiring a better estimates of the sparse coding coefficients of each corresponding noiseless SAR image patch. Experimental results show that the proposed method has a good capability of speckle smoothing in homogeneous region, as well as edge and texture preservation.

Non-negative structural sparse representation for high-resolution hyperspectral imaging

Abstract: High resolution hyperspectral images have important applications in many areas, such as anomaly detection, target recognition and image classification. Due to the limitation of the sensors, it is challenging to obtain high spatial resolution hyperspectral images. Recently, the methods that reconstruct high spatial resolution hyperspectral images from the pair of low resolution hyperspectral images and high resolution RGB image of the same scene have shown promising results. In these methods, sparse non-negative matrix factorization (SNNMF) technique was proposed to exploit the spectral correlations among the RGB and spectral images. However, only the spectral correlations were exploited in these methods, ignoring the abundant spatial structural correlations of the hyperspectral images. In this paper, we propose a novel algorithm combining the structural sparse representation and non-negative matrix factorization technique to exploit the spectral-spatial structure correlations and nonlocal similarity of the hyperspectral images. Compared with SNNMF, our method makes use of both the spectral and spatial redundancies of hyperspectral images, leading to better reconstruction performance. The proposed optimization problem is efficiently solved by using the alternating direction method of multipliers (ADMM) technique. Experiments on a public database show that our approach performs better than other state-of-the-art methods on the visual effect and in the quantitative assessment.

A new optical surface measurement method with iterative sparsity-constrained threshold phase retrieval algorithm.

Abstract: Due to its low complexity and acceptable accuracy, phase retrieval technique has been proposed as an alternative to solve the classic optical surface measurement task. However, to capture the overall wave field, phase retrieval based optical surface measurement (PROSM) system has to moderate the CCD position during the multiple-sampling procedure. The mechanical modules of CCD movement may bring about unexpectable deviation to the final results. To overcome this drawback, we propose a new PROSM method based on spatial light modulator (SLM). The mechanical CCD movement can be replaced by an electrical moderation of SLM patterns; thus the deviation can be significantly suppressed in the new PROSM method. In addition, to further improve the performance, we propose a new iterative threshold phase retrieval algorithm with sparsity-constraint to effectively reconstruct the phase of wave field. Experimental results show that the new method provides a more simple and robust solution for the optical surface measurement than the traditional techniques and achieves higher accuracy.

Compressive Sensing Reconstruction for Sparse Signals with Convex Optimization

Abstract: The theory of compressive sampling (CS), also known as compressed sensing. It is a modern sensing scheme that goes against the common theory in data acquisition. The CS theory claims that one can recover images or signals from fewer samples or measurements than the traditional methods use. To achieve this recovery, CS theory depends on two basic principles: the first is the sparsity of signal, which relates to the signals of interest, and the incoherence, which relates to the sensing method. In this paper we will give a simple review on the CS theory and the analog to information (AIC) system will be discussed briefly supported with two examples of signal reconstruction from undersampled signals. Simulation results show the powerful of the CS reconstruction for both sparse in time and spars in frequency signals.

Orientation selectivity based structure for texture classification

Abstract: Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

Compressive Spectral Video Acquisition with Double-channel Complementary Coded Aperture

Abstract: Spectral video is crucial for monitoring of dynamic scenes, reconnaissance of moving targets, observation and tracking of living cells, etc. The traditional spectral imaging methods need multiple exposures to capture a full frame spectral image, which leads to a low temporal resolution and thus lose their value as spectral video. The new code aperture snapshot spectral imaging (CASSI) method has been emerging in recent years, which is suitable for spectral video acquisition, due to its high-speed snapshot and few-amount measurements. Based on the CASSI, this paper proposes a compressive spectral video acquisition method with double-channel complementary coded aperture. The method can achieve the spectral video with a high temporal resolution by directly sampling the 3D spectral scene with 2D array sensor in only one snapshot. Furthermore, by using the double-channel complementary coded aperture in compressive measurement and the sparse regularity in the optimization recovery together, we can obtain the higher PSNR and better visual effects compared with the single-channel CASSI. Simulation results demonstrate the efficacy of the proposed method.

Image super-resolution via adaptive filtering and regularization

Abstract: Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely, through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework. Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual fidelity compared with the state-of-the-art methods.