Sequence Of Low-resolution Images Research Articles

A Video Super Resolution Algorithm Based on Wavelet Coefficient

In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

Detection and Tracking of Vehicle Target Based on Super-Resolution Reconstruction and Variable Template Matching

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.

Kalman filter–based method for image superresolution using a sequence of low-resolution images

The Kalman filter is widely acknowledged as a linear optimal estimator. However, it is usually considered to have taxing computational and storage requirements in the field of image superresolution. This work presents an innovative Kalman filter–based method to solve the inherent problems of the Kalman filter and make it feasible for superresolution applications. Unlike other Kalman filter–based methods, we did not assume the pixels of high-resolution (HR) image are uncorrelated and the covariance matrix is diagonal for the calculation simplification. Instead, we take the spatial correlation of the pixels within a local block of HR image into consideration and propose a block diagonal assumption for the prediction covariance after a new construction way of the column vector which represents the HR image is presented. This significantly reduces the computational complexity during the Kalman filtering. Then, we develop a point-wise Kalman filter method to reduce the computational complexity and decrease the storage requirement. Furthermore, an iterative scheme is proposed to overcome the inaccurate initial estimation problem of superresolution reconstruction. The experiments with synthetic and real data show that the proposed method is effective and outperforms other superresolution methods.

Infrared super-resolution imaging based on compressed sensing

The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm’s application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.

A phase space model of Fourier ptychographic microscopy

A new computational imaging technique, termed Fourier ptychographic microscopy (FPM), uses a sequence of low-resolution images captured under varied illumination to iteratively converge upon a high-resolution complex sample estimate. Here, we propose a mathematical model of FPM that explicitly connects its operation to conventional ptychography, a common procedure applied to electron and X-ray diffractive imaging. Our mathematical framework demonstrates that under ideal illumination conditions, conventional ptychography and FPM both produce datasets that are mathematically linked by a linear transformation. We hope this finding encourages the future cross-pollination of ideas between two otherwise unconnected experimental imaging procedures. In addition, the coherence state of the illumination source used by each imaging platform is critical to successful operation, yet currently not well understood. We apply our mathematical framework to demonstrate that partial coherence uniquely alters both conventional ptychography's and FPM's captured data, but up to a certain threshold can still lead to accurate resolution-enhanced imaging through appropriate computational post-processing. We verify this theoretical finding through simulation and experiment.

A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment

A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low‐resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High‐resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal‐to‐noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill‐posed problems in reconstruction process.

Super-resolution image reconstruction for ultrasonic nondestructive evaluation

Ultrasonic testing is one of the most successful nondestructive evaluation (NDE) techniques for the inspection of carbon-fiber-reinforced polymer (CFRP) materials. This paper discusses the application of the iterative backprojection (IBP) super-resolution image reconstruction technique to carbon epoxy laminates with simulated defects to obtain high-resolution images for NDE. Super-resolution image reconstruction is an approach used to overcome the inherent resolution limitations of an existing ultrasonic system. It can greatly improve the image quality and allow more detailed inspection of the region of interest (ROI) with high resolution, improving defect evaluation and accuracy. First, three artificially simulated delamination defects in a CFRP panel were considered to evaluate and validate the application of the IBP method. The results of the validation indicate that both the contrast-tonoise ratio (CNR) and the peak signal-to-noise ratio (PSNR) value of the super-resolution result are better than the bicubic interpolation method. Then, the IBP method was applied to the low-resolution ultrasonic C-scan image sequence with subpixel displacement of two types of defects (delamination and porosity) which were obtained by the micro-scanning imaging technique. The result demonstrated that super-resolution images achieved better visual quality with an improved image resolution compared with raw C-scan images.

Super-resolution reconstruction face recognition based on multi-level FFD registration

Super-resolution (SR) reconstruction is an effective method to solve the problem, that the face image resolution is too low to be recognized in video, but the non-rigid change of deformed face and expression changes greatly affect the accuracy of registration and reconstruction. To solve these problems, a method of multi-level model free form deformation (FFD) elastic registration algorithm based on B spline is proposed. It first use low-resolution FFD grid for global registration, to emphasize the contribution of edge information for registration, we introduce edge registration measure into the sum of squared difference (SSD) criterion. Then divide the global registration image and reference image into a series of corresponding sub-image pairs and calculate the correlation coefficient of each pair; at the same time, we do local registration with high-resolution FFD grid to the small value correlation coefficient sub-image pairs. In the registration process of optimization, the paper uses adaptive step length gradient descent method algorithm based on chaotic variables to improve optimization efficiency. After registration, the algorithm of project onto convex sets (POCS) is used to reconstruct SR face image through several low resolution image sequences, and then recognized these SR face images by support vector machines (SVM) classifier. Experimental results from standard video database and self-built video database show that this method can register and reconstruct face image accurately in the condition of great face deformation and expression change, while the face recognition accuracy is also improved.

Contour superresolved imaging of static ground targets using satellite platform

We propose a method for increasing the contour resolution of static ground targets and to overcome the diffraction limit of an optical system installed on top of a satellite. The resolution improvement is obtained by using a sequence of low-resolution images taken from different angles realized by the movement of the satellite platform. The superresolving process is obtained by the generation of relative movement between the inspected object and the a priori known high-resolution background. The relative movement is caused because the images are taken from different angles. The captured set of low-resolution images are decoded by the a priori known high-resolution background obtained from a set of reference images taken only once by a high-resolution camera. The proposed concept is demonstrated via Matlab simulation and laboratory experiments.

A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization

Multiframe image super-resolution is a technique to reconstruct a high-resolution image by fusing a sequence of low-resolution images of the same scene. In this paper, we propose a new multiframe image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new adaptive norm combining the advantages of traditional L1 and L2 norms is used in both terms. The fidelity term is then formed by an adaptive strategy depending on the accuracies of the estimated low-resolution image observation models. This strategy serves to adaptively weight low-resolution images according to their reliability and can add robustness in practical implementation of super-resolution. The proposed regularization term can preserve sharp edges well without producing visual artifacts. Our experimental results using both synthetic and real data show the performance improvement of the proposed algorithm over other methods.

Coordinate-descent super-resolution and registration for parametric global motion models

We present a method for simultaneously obtaining registration and super-resolution from a sequence of low resolution images, based on a coordinate-descent approach. The novelty of the algorithm resides on the registration step, which can be applied easily to any parametric global motion model. We prove the validity of the model with synthetic and real data experiments, being of special interest the good performance achieved in the difficult case of images registered under a projective transformation.

Face recognition performance with superresolution

With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.

Variational method for super-resolution optical flow

The motion fields in an image sequence observed by a car-mounted imaging system depend on the positions in the imaging plane. Since the motion displacements in the regions close to the camera centre are small, for accurate optical flow computation in this region, we are required to use super-resolution of optical flow fields. We develop an algorithm for super-resolution optical flow computation. Super-resolution of images is a technique for recovering a high-resolution image from a low-resolution image and/or image sequence. Optical flow is the appearance motion of points on the image. Therefore, super-resolution optical flow computation yields the appearance motion of each point on the high-resolution image from a sequence of low-resolution images. We combine variational super-resolution and variational optical flow computation in super-resolution optical flow computation. Our method directly computes the gradient and spatial difference of high-resolution images from those of low-resolution images, without computing any high-resolution images used as intermediate data for the computation of optical flow vectors of the high-resolution image.

Super Resolution for Remote Sensing Images Based on a Universal Hidden Markov Tree Model

In this paper, we propose a new super resolution (SR) method called the maximum a posteriori based on a universal Hidden Markov Tree (HMT) model for remote sensing images. The HMT theory is first used to set up a prior model for reconstructing super resolved images from a sequence of warped, blurred, subsampled, and noise-contaminated low-resolution (LR) images. Because the wavelet coefficients of images can be well characterized as a mixed Gaussian distribution, an HMT model is better able to capture the dependences between multiscale wavelet coefficients. The new method is tested first against simulated LR views from a single Landsat7 panchromatic scene and, then, with actual data from four Landsat7 panchromatic images captured on different dates. Both tests show that our method achieves better SR results both visually and quantitatively than other methods.

Superresolution imaging in optical tweezers using high-speed cameras

High-speed cameras are reliable alternatives for the direct characterization of optical trap force and particle motion in optical tweezers setups, replacing indirect motion measurements often performed by quadrant detectors. In the present approach, subpixel motion data of the trapped particle is retrieved from a high-speed low-resolution video sequence. Due to the richness structure of motion diversity of microscopic trapped particles, which are subjected to a Brownian motion, we propose to also use the obtained motion information for tackling the inherent lack of resolution by applying superresolution algorithms on the low-resolution image sequence. The obtained results both for trapping calibration beads and for living bacteria show that the proposed approach allows the proper characterization of the optical tweezers by obtaining the real particle motion directly from the image domain, while still providing high resolution imaging.

Support-assisted optical superresolution of low-resolution image sequences: the one-dimensional problem

We analyze the problem of optical superresolution (OSR) of a one-dimensional (1D) incoherent spatial signal from undersampled data when the support of the signal is known in advance. The present paper corrects and extends our previous work on the calculation of Fisher information (FI) and the associated Cramer-Rao lower bound (CRB) on the minimum error for estimating the signal intensity distribution and its Fourier components at spatial frequencies lying beyond the optical band edge. The faint-signal and bright-signal limits emerge from a unified noise analysis in which we include both additive noise of detection and shot noise of photon counting via an approximate Gaussian statistical distribution. For a large space-bandwidth product, we derive analytical approximations to the exact expressions for FI and CRB in the faint-signal limit and use them to argue why achieving any significant amount o unbiased bandwidth extension in the presence of noise is a uniquely challenging proposition. Unlike previous theoretical work on the subject of support-assisted bandwidth extension, our approach is not restricted to specific forms of the system transfer functions, and provides a unified analysis of both digital and optical superresolution of undersampled data.

Resolution-enhanced subpixel phase retrieval method

Phase retrieval is a wavefront sensing method that uses a series of intensity images to reconstruct the wavefront. The resolution of phase retrieval testing is limited mainly by the resolution of intensity images captured by CCD cameras. A subpixel phase retrieval method is presented to retrieve the wave field at subpixel resolution by using the information of a sequence of low-resolution images captured along the propagation direction. In this method, the sampling interval for the wave field under test is smaller than the CCD pixel size in phase reconstruction. The wave field is recovered at subpixel resolution by utilizing the energy conservation relationship between CCD pixels and their subpixels by the subpixel phase retrieval (SPR) algorithm. Numerical experiments have shown that more than a fourfold resolution enhancement can be achieved. The method has also been studied in some experiments under noisy and off-axis conditions. A mirror surface testing experiment was conducted to demonstrate the performance of SPR in the real world. The results of these experiments have shown the effectiveness and robustness of this method. SPR allows low-resolution images to be used to retrieve high-resolution wave fields and will be useful in testing wave fields from large objects.

Learning Generative Models for Multi-Activity Body Pose Estimation

We present a method to simultaneously estimate 3D body pose and action categories from monocular video sequences. Our approach learns a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Body poses are modelled on a low-dimensional manifold obtained by Locally Linear Embedding dimensionality reduction. In addition, we learn a prior model of likely body poses and a dynamical model in this pose manifold. Sparse kernel regressors capture the nonlinearities of this mapping efficiently. Within a Recursive Bayesian Sampling framework, the potentially multimodal posterior probability distributions can then be inferred. An activity-switching mechanism based on learned transfer functions allows for inference of the performed activity class, along with the estimation of body pose and 2D image location of the subject. Using a rough foreground segmentation, we compare Binary PCA and distance transforms to encode the appearance. As a postprocessing step, the globally optimal trajectory through the entire sequence is estimated, yielding a single pose estimate per frame that is consistent throughout the sequence. We evaluate the algorithm on challenging sequences with subjects that are alternating between running and walking movements. Our experiments show how the dynamical model helps to track through poorly segmented low-resolution image sequences where tracking otherwise fails, while at the same time reliably classifying the activity type.

People detection in low-resolution video with non-stationary background

In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].

On fusion of range and intensity information using Graph-Cut for planar patch segmentation

Planar patch detection aims at simplifying data from 3D imaging sensors to a more compact scene description. We propose a fusion of intensity and depth information using Graph-Cut methods for this problem. Different known algorithms are additionally evaluated on low-resolution high-frame rate image sequences and used as an initialisation for the Graph-Cut approach. In experiments, we show a significant improvement of the detected patch boundaries after the refinement with our method.