Super-resolution Reconstruction Algorithm Research Articles

A Computationally Efficient Super-Resolution Reconstruction Algorithm Based On The Hybird Interpolation

In order to release the limitations on the computational complexity and the detail missing of the traditional interpolation algorithm, this paper proposes a double channel hybrid interpolation algorithm for super-resolution reconstruction that can not only reduce the complexity effectively but also keep the details of the image sufficiently. The improved algorithm involves following methods: first, the parallel processing is used in this project; second, the cubic B-spline linear interpolation is introduced in the low frequency domain; third, the edge interpolation algorithm is applied in the high frequency details. The simulation and experiments both indicate that this algorithm is able to process the image in real time with details preserved.

Phase regularisation for single image super-resolution reconstruction

A novel single image super-resolution reconstruction algorithm is proposed. Based on the geometrical model on the phases of complex wavelet coefficients of the image, a new image prior model leads to a powerful regularisation strategy for image super-resolution reconstruction, which is applicable to general image structures. Experimental results show that the method is effective at preserving the visual detailed information.

Super-resolution reconstruction and higher-degree function deformation model based matching for Chang’E-1 lunar images

This article intends to solve the matching problem of 2C level lunar images by Chang’E-1 (CE-1) lunar probe satellite. A line-scanner image matching method is proposed which represents deformation by the quadric function along the camera motion direction and bases on the deformation model for a relief terrain’s imaging on sensors of the satellite borne three-line scanner camera. A precise matching is carried out for the normal view, the frontward view, and the backward view images of the CE-1 by combining the proposed method with the standard correlation method. A super-resolution (SR) reconstruction algorithm based on the wavelet interpolation of non-uniformly sampled data is also adopted to realize SR reconstruction of CE-1 lunar images, which adds the recognizable targets and explores CE-1 lunar images to the full.

A super-resolution reconstruction algorithm for surveillance images

In many surveillance video applications, it is of interest to recognize a region of interest (ROI), which often occupies a small portion of a low-resolution, noisy video. This paper proposes an edge-preserving maximum a posteriori (MAP) estimation based super-resolution algorithm using a weighted directional Markov image prior model for a ROI from more than one low-resolution surveillance image. Conjugate gradient (CG) optimization based on standard operations on images is then developed to improve the computational efficiency of the algorithm. The proposed algorithm is tested on different series of surveillance images. The experimental results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.

Video Enhancement Based on A Robust Hampel Iterative SRR with a General Observation Model

This paper proposes a novel robust Super Resolution Reconstruction (SRR) framework that can enhance a real complex video sequence and is applicable to any noise models. Although SRR algorithms have received considerable attention within the traditional research community, these algorithms are typically very sensitive to their assumed model of data and noise, which limits their utility. The real noise models that corrupt the measured sequence are unknown; consequently, SRR algorithms using L1 or L2 norm may degrade the image sequence rather than enhance it therefore the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes a SRR framework based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. In order to tolerate to any noise models, the Hampel norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, and removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. Moreover, in order to cope with real video sequences and complex motion sequences, this paper proposes a SRR General Observation Model (GOM or a±ne block-based transform) devoted to the case of nonisometric inter-frame motion. In the experimental section, the proposed framework can enhance real complex motion sequences, such as Suzie and Foreman sequence, and con¯rm the effectiveness of our algorithm and demonstrate its superiority to other SRR algorithms based on L1 and L2 norm for several noise models (such as AWGN, Poisson noise, Salt & Pepper noise and Speckle noise) at several noise power.

A multi-frame image super-resolution method

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction.

Achieving super‐resolution X‐ray imaging with mobile C‐arm devices

The term super-resolution refers to the process of combining a set of low-resolution images into a high-resolution image using image processing methods. This work is concerned with the reconstruction of high-resolution X-ray images. Specifically, we address the problem of acquiring X-ray images from multiple, very close view points. We propose to use a novel experimental robotic C-arm device to create high-resolution X-ray images. For this purpose, we suggest different strategies for acquiring multiple low-resolution images, and we provide the steps to achieve acquisition-error compensation. Compared to visible light images, X-ray images have the particularity that parallax effects render super-resolution very difficult. Using the acquired multi-frame data, we evaluate recent well-known super-resolution reconstruction algorithms. The same algorithms are evaluated based on synthetic 3D phantom data and real X-ray images. In experiments with both synthetic and real projection data, we successfully reconstruct up to four times higher-resolution images. These images reveal structures and details which are not perceivable in the low-resolution images. The advantage of super-resolution techniques for X-ray is the potential reduction of radiation dose for patients and medical personnel. Potential medical applications include the diagnosis of early-stage osteoporosis and the detection of very small calcifications.

Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images

In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L1 norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.

Thin infrared imaging systems through multichannel sampling

The size of infrared camera systems can be reduced by collecting low-resolution images in parallel with multiple narrow-aperture lenses rather than collecting a single high-resolution image with one wide-aperture lens. We describe an infrared imaging system that uses a three-by-three lenslet array with an optical system length of 2.3 mm and achieves Rayleigh criteria resolution comparable with a conventional single-lens system with an optical system length of 26 mm. The high-resolution final image generated by this system is reconstructed from the low-resolution images gathered by each lenslet. This is accomplished using superresolution reconstruction algorithms based on linear and nonlinear interpolation algorithms. Two implementations of the ultrathin camera are demonstrated and their performances are compared with that of a conventional infrared camera.

NEW TECHNIQUES TO CONQUER THE IMAGE RESOLUTION ENHANCEMENT PROBLEM

This paper presents some new techniques for high resolution (HR) image processing and compares between them. The paper focuses on two main topics, image interpolation and image super- resolution. By image interpolation, we mean extracting an HR image from a single Degraded low resolution (LR) image. Polynomial based image interpolation is reviewed. Some new techniques for adaptive image interpolation and inverse image interpolation are presented. The other topic treated in this paper is image super-resolution. By image super resolution, we mean extracting a single HR image either from multiple observations or multiple frames. The paper focuses on the problem of image super resolution using wavelet fusion and presents several super resolution reconstruction algorithms based on the idea of wavelet fusion.

Blur Identification and Image Super-Resolution Reconstruction Using an Approach Similar to Variable Projection

Super-resolution reconstruction (SRR) produces a high-resolution image from multiple low-resolution images. Many image SRR algorithms assume that the blurring process, i.e., point spread function (PSF) of the imaging system is known in advance. However, the blurring process is not known or is known only to within a set of parameters in many practical applications. In this letter, we propose an approach for solving the joint blur identification and image SRR based on the principle similar to the variable projection method. The approach can avoid some shortcomings of cyclic coordinate descent optimization procedure. We also propose an efficient implementation based on Lanczos algorithm and Gauss quadrature theory. Experimental results are presented to demonstrate the effectiveness of our method.

A self-adaptive motion estimation algorithm for superresolution reconstruction of multiframe SPOT panchromatic images

High spatial resolution of imaging sensors onboard Earth observation satellites is often critical for land use and land cover mapping. In this paper, we propose a self-adaptive motion estimation algorithm that combines the consistency characteristics of image distortion, high accuracy feature point extraction, and image matching techniques to achieve subpixel accuracy of motion estimation. Using the wavelet superresolution and the modulation transfer function (MTF), we implemented the superresolution reconstruction from multiframe Satellite pour l’Observation de la Terre (SPOT-4) panchromatic images under a framework of interpolation–restoration. The results show that this algorithm is promising for reconstruction of a higher resolution frame.

Characteristics of infrared imaging systems that benefit from superresolution reconstruction

There have been numerous applications of superresolution reconstruction algorithms to improve the range performance of infrared imagers. These studies show there can be a dramatic improvement in range performance when superresolution algorithms are applied to undersampled imager outputs. These occur when the imager is moving relative to the target, which creates different spatial samplings of the field of view for each frame. The degree of performance benefit is dependent on the relative sizes of the detector/spacing and the optical blur spot in focal plane space. The minimum blur spot size achievable on the focal plane is dependent on the system F/number. Hence, we provide a range of these sensor characteristics, for which there is a benefit from superresolution reconstruction algorithms. Additionally, we quantify the potential performance improvements associated with these algorithms. We also provide three infrared sensor examples to show the range of improvements associated with provided guidelines.

A Lorentzian Stochastic Estimation for a Robust Iterative Multiframe Super-Resolution Reconstruction with Lorentzian-Tikhonov Regularization

Recently, there has been a great deal of work developing super-resolution reconstruction (SRR) algorithms. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation, therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore, the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper first comprehensively reviews the SRR algorithms in this last decade and addresses their shortcomings, and latter proposes a novel robust SRR algorithm that can be applied on several noise models. The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. For removing outliers in the data, the Lorentzian error norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image. Moreover, Tikhonov regularization and Lorentzian-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norms for several noise models such as noiseless, additive white Gaussian noise (AWGN), poisson noise, salt and pepper noise, and speckle noise.

A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video

Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.

Assessment of surface roughness based on super resolution reconstruction algorithm

In this work, an attempt has been made to use a super resolution image processing algorithm for preprocessing the images over and above existing image quality enhancement techniques. The improved quality images processed using a machine vision system have been used to assess the quality of the surfaces. To ensure the validity of the approach the roughness values quantified using these images are then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness is performed using two Fourier transform parameters (major peak frequency and principal component magnitude squared value) and the standard deviation of gray level intensity values. Then the group method of data handling (GMDH) technique was used to obtain an analytical relationship of the roughness parameters calculated using the digital surface image and the stylus instrument values. We present in this paper an analysis based on the comparison to make sure that the present approach of estimation of surface finish based on the digital processed image could be implemented in practice.

A greatest common divisor approach to blind super-resolution reconstruction of images

An algorithm for blind super-resolution reconstruction of a single image from multiple degraded observations is developed. The algorithm depends on estimating the 2D greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations. The purpose of generating this combinational image is to obtain a new image with a higher signal to noise ratio, and a blurring operator that is co-prime with all the blurring operators of the available observations. The 2D GCD is then estimated between the new image and each observation and thus the effect of noise on the estimation process is reduced. The results of each 2D GCD process are fused to form a single reconstructed image, which is then interpolated subject to local regularization to form a high-resolution (HR) image. Results show that the proposed algorithm succeeds in estimating an HR image from noisy blurred observations in the case of relatively co-prime unknown blurring operators.

Video super-resolution using controlled subpixel detector shifts

Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. The resolution of videos can be computationally enhanced by moving the camera and applying super-resolution reconstruction algorithms. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super-resolution. The conclusion is that, in order to achieve the highest resolution, motion blur should be avoided. Motion blur can be minimized by sampling the space-time volume of the video in a specific manner. We have developed a novel camera, called the "jitter camera," that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.

Super-resolution reconstruction of image sequence using multiple motion estimation fusion

Super-resolution reconstruction algorithm produces a high-resolution image from a low-resolution image sequence. The accuracy and the stability of the motion estimation (ME) are essential for the whole restoration. In this paper, a new super-resolution reconstruction algorithm is developed using a robust ME method, which fuses multiple estimated motion vectors within the sequence. The new algorithm has two major improvements compared with the previous research. First, instead of only two frames, the whole sequence is used to obtain a more accurate and stable estimation of the motion vector of each frame; second, the reliability of the ME is quantitatively measured and introduced into the cost function of the reconstruction algorithm. The algorithm is applied to both synthetic and real sequences, and the results are presented in the paper.

Three-dimensional image sensing and reconstruction with time-division multiplexed computational integral imaging.

A method to compute high-resolution three-dimensional images based on integral imaging is presented. A sequence of integral images (IIs) is captured by means of time-division multiplexing with a moving lenslet array technique. For the acquisition of each II, the location of the lenslet array is shifted periodically within the lenslet pitch in a plane perpendicular to the optical axis. The II sequence obtained by the detector array is processed digitally with superresolution reconstruction algorithms to obtain a reconstructed image, appropriate to a viewing direction, which has a spatial resolution beyond the optical limitation.