Resolution Reconstruction Algorithm Research Articles

Accuracy of ultra-high resolution and virtual non-calcium reconstruction algorithm for stenosis evaluation with photon-counting CT: results from a dynamic phantom study

BackgroundWe compared ultra-high resolution (UHR), standard resolution (SR), and virtual non-calcium (VNCa) reconstruction for coronary artery stenosis evaluation using photon-counting computed tomography (PC-CT).MethodsOne vessel phantom (4-mm diameter) containing solid calcified lesions with 25% and 50% stenoses inside a thorax phantom with motion simulation underwent PC-CT using UHR (0.2-mm slice thickness) and SR (0.6-mm slice thickness) at heart rates of 60 beats per minute (bpm), 80 bpm, and 100 bpm. A paired t-test or Wilcoxon test with Bonferroni correction was used.ResultsFor 50% stenosis, differences in percent mean diameter stenosis between UHR and SR at 60 bpm (51.0 vs 60.3), 80 bpm (51.7 vs 59.6), and 100 bpm (53.7 vs 59.0) (p ≤ 0.011), as well as between VNCa and SR at 60 bpm (50.6 vs 60.3), 80 bpm (51.5 vs 59.6), and 100 bpm (53.7 vs 59.0) were significant (p ≤ 0.011), while differences between UHR and VNCa at all heart rates (p ≥ 0.327) were not significant. For 25% stenosis, differences between UHR and SR at 60 bpm (28.0 vs 33.7), 80 bpm (28.4 vs 34.3), and VNCa vs SR at 60 bpm (29.1 vs 33.7) were significant (p ≤ 0.015), while differences for UHR vs SR at 100 bpm (29.9 vs 34.0), as well as for VNCa vs SR at 80 bpm (30.7 vs 34.3) and 100 bpm (33.1 vs 34.0) were not significant (p ≥ 0.028).ConclusionStenosis quantification accuracy with PC-CT improved using either UHR acquisition or VNCa reconstruction.Relevance statementPC-CT offers to scan with UHR mode and the reconstruction of VNCa images both of them could provide improved coronary stenosis quantification at increased heart rates, allowing a more accurate stenosis grading at low and high heart rates compared to SR.Key PointsEvaluation of coronary stenosis with conventional CT is challenging at high heart rates.PC-CT allows for scanning with ECG-gated UHR and SR modes.UHR and VNCa images were compared in a dynamic phantom.UHR improves stenosis quantification up to 100 bpm.VNCa reconstruction improves stenosis evaluation up to 80 bpm.Graphical

A high contrast and resolution reconstruction algorithm in quantitative photoacoustic tomography

A framework for reconstruction of optical diffusion and absorption coefficients in quantitative photoacoustic tomography is presented. This framework is based on a Tikhonov-type functional with a regularization term promoting sparsity of the absorption coefficient and a prior involving a Kubelka–Munk absorption–diffusion relation that allows to obtain superior reconstructions. The reconstruction problem is formulated as the minimization of this functional subject to the differential constraint given by a photon-propagation model. The solution of this problem is obtained by a fast and robust sequential quadratic hamiltonian algorithm based on the Pontryagin maximum principle. Results of several numerical experiments demonstrate that the proposed computational strategy is able to obtain reconstructions of the optical coefficients with high contrast and resolution for a wide variety of objects.

Abstract 2329: Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data

Abstract Background: Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited resolution of ST such as Visium hinders the accurate identification of cancerous regions. To address these issues, we have developed a novel approach for accurately classifying regions of the tumor by using deep image prior (DIP) algorithm to convert Visium data into high-resolution images. Methods: We proposed SuperST, which utilizes deep image prior to transform low-resolution ST data into high-resolution images, providing more accurate spatial marker expression patterns. This method was used to define cancer regions by combining with a pre-trained deep learning algorithm that recognizes spatial patterns. ST data of hepatocellular carcinoma (HCC) patients were used to apply SuperST. Firstly, 400 spatially variable genes were identified. SuperST leverages the deep image prior (DIP) algorithm and a U-Net neural network to transform ST data into high-resolution ones without needing ground-truth high-resolution data. The final high-resolution images of gene expression data were used to extract 512-dimensional feature vectors for each gene via a pre-trained convolutional neural network model (VGG16). Subsequently, we apply K-means clustering to these features to identify transcriptionally distinct tissue regions considering spatial information and compared with the human-labeled cancer regions. Results: The cancer region detection using spatial gene expression data and our algorithm yielded results that substantially align with the human annotations on the H&E images. Furthermore, compared to the cancer definition obtained using the CopyKat and SPACET algorithms, our results were more spatially consistent for cancerous regions as well as providing high-resolution image-level definition of cancer region margin. This indicates that our algorithm better utilizes spatial information and more effectively overcomes the low resolution of ST data. Conclusion: This research demonstrates the effective application of our SuperST algorithm to the challenging problem of cancer region detection only using spatial gene expression data. It suggests that our algorithm could be utilized for a variety of problems in spatial biology in cancer research. Citation Format: Jeongbin Park, Seungho Cook, Dongjoo Lee, Jinyeong Choi, Seongjin Yoo, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2329.

Wavelength-multiplexing high-spectral resolution imaging based on Fourier ptychographic microscopy

A wide field of view (FOV), high spectral resolution, and high spatial resolution are the targets of optical microscopic imaging. Fourier ptychographic microscopy (FPM) imaging can solve the contradiction between a wide FOV and spatial resolution. However, the spectral resolution of the samples is not accessible to typical LED-based FPM. Here, we report a wavelength-multiplexing high-spectral resolution reconstruction strategy based on the FPM of the halogen lamp. In contrast to the typical FPM setting, we can obtain spectral information of arbitrary wavelengths and greatly improve the sampling efficiency. We optimize and combine the wavelength-multiplexed decoupled algorithm with the high-spectral resolution reconstruction algorithm. We show that, by using our strategy, we can better reconstruct the image with a spectral resolution of 5 nm and maintain wide FOV and high spatial resolution. We believe the reported strategy may provide ideas for FPM-based spectral microimaging.

Improved Double Regression Nonlinear Image Super Resolution Model

Abstract The existing super resolution reconstruction methods are mainly divided into traditional super resolution reconstruction and deep learning super resolution reconstruction. The main problem faced by traditional super resolution reconstruction algorithms, such as image enlargement and space transformation, is how to establish the mapping relationship between the input image and the target image, and express the pixel value of the target image through the mapping relationship. As a prominent problem, the difficulty of super resolution reconstruction lies in the fact that there is no realizable matrix relationship between one - to - many mapping relationships. Based on the U-Net network framework, this paper improves the jump-connected modules. By using the combination of convolutional layer, activation layer and residual channel block, the overall module operation efficiency is increased by 2.4%, the overall PNSR is increased by 0.49db, and the running speed is increased by 0.3ms on average when processing a single image compared with other classical models.

Intelligent Algorithm-Based MRI Image Features for Evaluating the Effect of Nursing on Recovery of the Neurological Function of Patients with Acute Stroke.

The aim of this study is to analyze the application of early rehabilitation nursing in nursing intervention of neurological impairment among patients with acute ischemic stroke. 116 patients with acute ischemic stroke were selected as the research subjects in this paper. The patients were divided into 58 experimental (early rehabilitation care) and 58 control (routine rehabilitation care) groups according to the difference of care protocols, all of which were performed magnetic resonance imaging on. An image resolution reconstruction algorithm on the basis of deep convolutional neural network is proposed for MRI image processing. The results show that peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the included algorithm were remarkably greater than those of compressed sensing (CS) algorithm and nonlocal similarity and block low rank prior-based NSBL algorithm. Running time was shorter than that of the latter two algorithms (P < 0.05). The neurological impairment scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in the control group (P < 0.05). The Barthel indexes of patients in the experimental group 3 and 5 weeks after treatment were obviously higher than those of patients in the control group (P < 0.05). FugI-Meyer assessment (FMA) and Disability of Arm-Shoulder-Hand (DASH) scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in control group (P < 0.05). The results show that the deep learning algorithm for MRI image processing performance is better than the traditional algorithm. It not only improves the image quality but also improves the processing efficiency. Early rehabilitation nursing and routine rehabilitation nursing can effectively improve the neurological deficit symptoms, limb motor function, and daily living ability of patients with acute ischemic stroke, and the effect of early rehabilitation nursing is the best.

Automated LoD-2 model reconstruction from very-high-resolution satellite-derived digital surface model and orthophoto

Digital surface models (DSM) generated from multi-stereo satellite images are getting higher in quality owing to the improved data resolution and photogrammetric reconstruction algorithms. Very-high-resolution (VHR, with sub-meter level resolution) satellite images effectively act as a unique data source for 3D building modeling, because it provides a much wider data coverage with lower cost than the traditionally used LiDAR and airborne photogrammetry data. Although 3D building modeling from point clouds has been intensively investigated, most of the methods are still ad-hoc to specific types of buildings and require high-quality and high-resolution data sources as input. Therefore, when applied to satellite-based point cloud or DSMs, these developed approaches are not readily applicable and more adaptive and robust methods are needed. As a result, most of the existing work on building modeling from satellite DSM achieves LoD-1 generation. In this paper, we propose a model-driven method that reconstructs LoD-2 building models following a “decomposition-optimization-fitting” paradigm. The proposed method starts building detection results through a deep learning-based detector and vectorizes individual segments into polygons using a “three-step” polygon extraction method, followed by a novel grid-based decomposition method that decomposes the complex and irregularly shaped building polygons to tightly combined elementary building rectangles ready to fit elementary building models. We have optionally introduced OpenStreetMap (OSM) and Graph-Cut (GC) labeling to further refine the orientation of 2D building rectangle. The 3D modeling step takes building-specific parameters such as hip lines, as well as non-rigid and regularized transformations to optimize the flexibility for using a minimal set of elementary models. Finally, roof type of building models s refined and adjacent building models in one building segment are merged into the complex polygonal model. Our proposed method has addressed a few technical caveats over existing methods, resulting in practically high-quality results, based on our evaluation and comparative study on a diverse set of experimental datasets of cities with different urban patterns. (codes /binaries may be available under this GitHub page: https://github.com/GDAOSU/LOD2BuildingModel).

RETRACTED ARTICLE: News propaganda of rural rainfall slope stability and environmental protection based on image reconstruction

Image resolution restoration technology is one of the main researches in the field of image processing. In recent years, image resolution reconstruction algorithms based on convolutional neural networks have attracted widespread attention. However, many modern models and algorithms are not enough to use the image function, and the reconstruction effect is not ideal. In this article, the image resolution restoration algorithm is explored and improved to achieve high-resolution image restoration. In this paper, the typical representative landslide of loess mudstone and typical landslide of accumulation layer are used as the research object. The SEEP/W module, SLOPE module, and FLAC-3D software in GeoStudio are used to analyze the landslide based on the step-by-step coupling path of rainfall infiltration and landslide deformation. Stability trend and deformation failure mechanism under different rainfall conditions. China attaches great importance to the ecological environment, and protecting the environment has become an important task for the country and every citizen. Human behavior will more or less affect the environment. In order to make a good contribution to environmental protection, human cognition must be improved. In addition, in order to raise people's awareness of environmental protection, it is necessary for the country to increase mainstream media propaganda. News media has a wide range of influence and high transparency, which can help promote environmental work. As we all know, the establishment of ecological civilization is inseparable from environmental protection, and environmental protection is inseparable from the support of news and propaganda. Therefore, based on image reconstruction, this article studies the stability of rural rainfall slopes, discusses the importance of news propaganda to environmental protection, and analyzes how to use news propaganda to carry out environmental work.

Single Image Super Resolution Reconstruction Algorithm Based on Deep Learning

Super resolution image reconstruction technology is the core technology in image processing, which provides technical support for most of the intelligent devices for target tracking and target detection. However, the existing image acquisition stage will cause extra high cost to improve the image resolution. Optimization algorithm is one of the best ways to solve this kind of problem. At present, there is no substantial breakthrough in this field in China. Therefore, this paper studies single image super-resolution reconstruction algorithm based on deep learning. In this paper, the application of super-resolution reconstruction algorithm in single image is discussed. The analysis shows that the application of deep learning in this field is shallow and has a large optimization space. Especially for the existing main problems, the traditional algorithm is optimized and improved. According to the demand of resolution reconstruction, combined with the latest deep learning method, the accuracy and robustness of the algorithm are further improved. At the same time, it effectively improves the comprehensive performance of the model. In order to further verify the actual performance of the proposed algorithm, the corresponding comparative experiments are established. The experimental results show that the proposed algorithm has obvious advantages over the traditional fsrcnn algorithm, especially when the PSNR of dataset 4 is 2, 3, and 4 times, it can increase 0.41 dB, 0.58 dB and 0.52 DB respectively. Analysis shows that this algorithm has obvious advantages and achieves ideal results.

Super Resolution Reconstruction Algorithm of UAV Image Based on Residual Neural Network

With the development of convolutional neural network, video super-resolution algorithm has achieved remarkable success. Because the dependence between frames is complex, traditional methods lack the ability to model the complex dependence, and it is difficult to estimate and compensate the motion accurately in the process of video super-resolution reconstruction. Therefore, a reconstruction network based on optical flow residuals is proposed. In low resolution space, the dense residual network is used to obtain the complementary information of adjacent video frames, and then the optical flow of high-resolution video frames is predicted through the pyramid structure, and then the low resolution video frames are transformed into high-resolution video frames through the sub-pixel convolution layer, The high-resolution video frame is compensated with the predicted high-resolution optical flow. Finally, it is input into the super-resolution fusion network to get better effect. A new loss function training network is proposed to better constrain the network. Experimental results on public data sets show that the reconstruction effect is improved in PSNR, structural similarity and subjective visual effect.

Image super resolution reconstruction algorithm based on generative countermeasure network

Image super resolution reconstruction algorithm based on generative countermeasure network

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Super resolution reconstruction algorithm of video image based on deep self encoding learning

Super resolution reconstruction of video image is a research hotspot in the field of image processing, and it is widely used in video surveillance, image processing, criminal analysis and other fields. Super resolution image reconstruction can reconstruct a high resolution image from low resolution images, and this technology has become a research hotspot in the field of image processing. In recent years, deep learning has been developed rapidly in the field of multimedia processing, and image super resolution restoration technology based on deep learning has gradually become the mainstream technology. In view of the existing image super-resolution algorithm problems, such as more parameters, larger amount of calculation, longer training time, blurred image texture, we use the deep self-coding learning method to improve it. We analyze the advantages and disadvantages of the existing technology from the network type, network structure, training methods and so on, and sort out the development of the technology. The experimental results show that the improved network model achieves better super-resolution results, and the subjective visual effect and objective evaluation index are improved obviously, and the image sharpness and edge sharpness are improved obviously.

P‐5.3: A super resolution reconstruction algorithm based on spatial autoregression regularization

In a lot of micro display applications, we need to enlarge and recognize a region of interest (ROI), which often get a low‐resolution image because of limited resolution source image. This paper proposed a novel image reconstruction algorithm based on spatial autoregression regularization and sparse representation. This reconstruction algorithm trained an image dictionary with the same sparse coefficient by sparse K‐SVD, and then import the autoregressive regularization item to construct the objective function which can realize local adaptive control of the image. In order to obtain further clear image, the degradation model was used which can realize the global constraints. The experimental results show that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.

Event identification for KM3NeT/ARCA

KM3NeT is a large research infrastructure consisting of a network of deep-sea neutrino telescopes. KM3NeT/ARCA will be the instrument detecting high-energy neutrinos with energies above 100 TeV. This instrument gives a new opportunity to observe the neutrino sky with very high angular resolution to be able to detect neutrino point sources. Furthermore it will be possible to probe the flavour composition of neutrino fluxes, and hence production mechanisms, with so-far unreached precision. Neutrinos produce different event topologies in the detector according to their flavour, interaction channel and deposited energy. Machine-learning algorithms are able to learn features of topologies to discriminate them. In previous analyses only two event types were regarded, namely the shower and track topology. With good timing resolution and precise reconstruction algorithms it is possible to separate into more event types, for example the double bang topology produced by tau neutrinos. The final goal is to distinguish all three neutrino flavors as much as possible. To resolve this issue the KM3NeT collaboration uses deep neural networks trained with Monte Carlo events of all neutrino types. This contribution shows the ability of KM3NeT/ARCA to classify events in more than two neutrino event topologies. Furthermore, the borders between detectable classes are shown, such as the minimum distance the tau has to travel before decaying into a tau neutrino to be detected as double bang event.

Influence of Scan Resolution, Thresholding, and Reconstruction Algorithm on Computed Tomography-Based Kinematic Measurements.

Radiographic data, including computed tomography (CT) and planar X-ray, is increasingly used for human and animal kinematic studies. There is a tendency toward using as high-resolution imaging as possible. Higher resolution imaging is one factor (in conjunction with the reconstruction algorithm), which may increase the precision of reconstructed three-dimensional (3D) surface models in representing true bone shape. However, to date no study has tested the effects of scan resolution, threshold, and 3D model reconstruction algorithm on the accuracy of bone kinematic results. The present study uses a novel method to do this where canine tarsal bones were positioned on a radiolucent Lego™ board and scanned before and after undergoing known translations and/or rotations. The digital imaging and communications in medicine (DICOM) images were acquired using two different CT scanning resolutions and processed using three different segmentation threshold levels and three different reconstruction algorithms. Using one bone as the reference bone, an iterative closest point (ICP) algorithm was used to register bones to a global co-ordinate system and allow measurement of other bone kinematics in terms of translations and rotations in and around the x-, y-, and z-axes. The measured kinematics were compared to the "known" kinematics, which were obtained from the Lego™ board's manufacturing standards and tolerances, to give accuracy error metrics for all bones. The results showed error in accuracy of measured kinematics was at subvoxel levels (less than 0.5 mm). Despite altering the volume and surface area of the 3D bone models, variation in resolution, segmentation threshold and reconstruction algorithm had no significant influence upon the accuracy of the calculated tarsal bone kinematics.

SU‐E‐I‐14: Avoiding a Common Pitfall in Performing MTF Measurements with High Resolution Kernels

Purpose:The modulation transfer function (MTF) is frequently used to assess high contrast spatial resolution for routine quality assurance in CT imaging. High resolution reconstruction kernels provide new challenges for MTF estimation. The purpose of this work is to demonstrate the effect of CT number saturation on MTF estimation.Methods:A small bead contained in a CATPHAN 600 phantom was imaged at two different slice thicknesses, 2.5 mm and 5.0 mm, on a GE CT 750 HD scanner using an HD Bone Plus reconstruction algorithm in a high resolution scan mode. The resulting images were 2‐D point spread functions (PSF's), which were integrated to create line spread functions, and Fourier transformed to yield MTF's.Results:Using a high resolution reconstruction algorithm and the 2.5 mm slice thickness, it was found that the CT number for several pixels in the PSF reached 3071, the maximum value allowed for this system. Using the same algorithm with the 5.0 mm slice, no pixel values reached the CT number limit. Comparing the two MTF's it was found that the MTF affected by CT number saturation had a 10% cut‐off that was shifted downwards, from 15.6 lp/cm to 14.9 lp/cm. Additionally, the MTF affected by CT number saturation exhibited a spurious lobe beyond the true limiting resolution of the CT scanner.Conclusion:The downwards shift in MTF cut‐off is a subtle but important effect to consider when attempting to measure MTF for routine quality assurance. While a downwards shift in MTF cut‐off is not easily identified, CT number saturation is readily identified in by examining the entire MTF curve and identifying the spurious lobe produced. CT number saturation is an important consideration when examining test objects to be used for MTF estimation when the algorithm being used is high resolution.This work was supported by the AAPM grant for Diagnostic Imaging Residencies

Edge Detection from Non-Uniform Fourier Data Using the Convolutional Gridding Algorithm

Detecting edges in images from a finite sampling of Fourier data is important in a variety of applications. For example, internal edge information can be used to identify tissue boundaries of the brain in a magnetic resonance imaging (MRI) scan, which is an essential part of clinical diagnosis. Likewise, it can also be used to identify targets from synthetic aperture radar data. Edge information is also critical in determining regions of smoothness so that high resolution reconstruction algorithms, i.e. those that do not smear over the internal boundaries of an image, can be applied. In some applications, such as MRI, the sampling patterns may be designed to oversample the low frequency while more sparsely sampling the high frequency modes. This type of non-uniform sampling creates additional difficulties in processing the image. In particular, there is no fast reconstruction algorithm, since the FFT is not applicable. However, interpolating such highly non-uniform Fourier data to the uniform coefficients (so that the FFT can be employed) may introduce large errors in the high frequency modes, which is especially problematic for edge detection. Convolutional gridding, also referred to as the non-uniform FFT, is a forward method that uses a convolution process to obtain uniform Fourier data so that the FFT can be directly applied to recover the underlying image. Carefully chosen parameters ensure that the algorithm retains accuracy in the high frequency coefficients. Similarly, the convolutional gridding edge detection algorithm developed in this paper provides an efficient and robust way to calculate edges. We demonstrate our technique in one and two dimensional examples.

Image Quality and Quantitative Accuracy of Small Animal PET Images: Role of Physical Effects Correction and Reconstruction Algorithm

Positron emission tomography (PET) provides important metabolic information about tracer kinetics. Photon attenuation, scattered radiation, spatial resolution limitation and reconstruction algorithm parameters are variables that influence image quality and quantitative accuracy. The objective of this work was to examine the impact of the combined correction of photon attenuation and scatter on the quality and quantitative accuracy of small animal PET images reconstructed using the analytic filtered back projection algorithm (FBP) and the iterative maximum a posteriori probability (MAP) technique at different values of the smoothing parameter β.Methods. A homogenous water phantomandthree different configurations of NEMA image quality phantom were employed using a small animal PET scanner (InveonTM, Siemens medical solutions, Inc). A 20 min PET/CT scans were performed for all phantoms using a list mode acquisition. All Data were sorted into 3D sinograms and reconstructed using 2D FBP after Fourier rebinning and also reconstructed using OSEM3D/MAP (2 iteration OSEM3D and 18 iterations using MAP). Figures of merit used were error of tracer estimate, recovery coefficient, percentage standard deviation, spillover ratio and image noise. Results. Based on object geometry and size, FBP with attenuation and scatter correction provides accurate tracer concentration within the range of 5% and possible improvement can be achieved using MAP reconstruction. Without correction, errors of activity quantitation is significantly high, on average 34.5±7.2% Recovery coefficient was also found to improve with proper tuning of the β value using MAP reconstruction. Spillover ratio indicated some variations between FBP and MAP in air and water compartments, which might be attributed to the performance of the scatter correction. Noise measurements were superior with MAP reconstruction especially at high β values, but this occurs with edge artifacts and slight distortion of some geometric structures. Very small values of the beta have better resolution characteristics but high values have increased smoothing effect and blurring appearance. Conclusion: Attenuation correction is important in small animal PET and allows for reliable tracer estimate. Spatial resolution is improved by MAP technique and low β values provide high resolution capabilities and recovery coefficients but high values improve noise levels. For a given application, proper selection and employment of the smoothing parameter β should be followed. Scatter correction need to be improved especially in large objects or simultaneous acquisition of multiple numbers of animals.

An Integrated Interpolation-based Super Resolution Reconstruction Algorithm for Video Surveillance

This paper aims at implementing an integrated super resolution reconstruction algorithm to interpolate the missing pixels in the grid to create a high resolution image for a specific purpose. One of the most important application areas of super resolution reconstruction is video surveillance for the purpose of public security. Although, the video surveillance technology are going under tremendous transformation from analog generation to IP-based systems. However, their replacement rate is still not encouraging due to installation and operating costs as well as low output video quality. To overcome this problem, we propose a new hybrid model to integrate super resolution reconstruction into video surveillance. Our proposed algorithm is based on interpolation of cropped low resolution frames extracted from a low quality video surveillance sequence for effective and efficient reconstruction of a high resolution license plate recognition image. Our super resolved image utilizing multiple frames provides far more detail information than any interpolated image from a single frame. The proposed algorithm requires a relatively small number of self extracted low resolution frames from a low quality input sequence. This is important for practical applications, because if a large number of low resolution frames were required the accumulation of imaging errors would adversely affect the reconstruction accuracy. We apply our proposed algorithm to a real sequence from video surveillance and compare our results with those obtained via well-established techniques. Experimental results show that the proposed algorithm performs much better than the conventional MISO super resolution techniques. We also noticed significant reduction in the computational cost and memory requirement during the whole reconstruction process.