Research on the Improvement of Image Super Resolution Reconstruction Algorithm Based on AWSRN Model

The paper proposes an image super resolution reconstruction (SRR) algorithm based on Undecimated Morphological Wavelet (UMW). As UMW can maintain more gradient information and eliminate more artificial blocks, it is used to decompose low resolution (LR) images. High frequency coefficients from the decomposition contain the gradients that reflect the horizontal, vertical, and diagonal domain information. Then both the low frequency and high frequency coefficients are interpolated to produce an extended group of coefficient sequence. Finally, the extended coefficients are fused and using inverse transformation to produce a super resolution image. Experimental results show that that the proposed algorithm have good performance in the image reconstruction quality and the computational efficiency.

There is a need for video encoders to generate bit streams of quality that matches human evaluation. This is often achieved by adopting perceptual quality metrics in video encoders. A huge body of work has been devoted to the development of perceptual video quality metrics that take the properties of human visual system into account. It is our interest in this paper to study how the existing video coding systems can benefit from such perceptual quality metrics. Specifically, we examine the existing perceptual video coding systems and introduce the concept of i§codec-friendliness, i¨ meaning how the perceptual quality metrics can be nicely incorporated into the coding process of standard video coding algorithms. We conclude the study by suggesting guidelines for the development of future quality metrics from the video coding perspective.

A Novel super resolution reconstruction algorithm from several low resolution images is proposed in this paper. Conventional super resolution image reconstruction techniques degrade the fidelity of the original digital image by populating a smaller portion of the available range of digital values with low pixel density, introducing noise in the reconstruction. The proposed system applies two stage decomposition of Discrete Wavelet Transform and combines the non-redundant information contained in multiple low-resolution images with different sub pixel shifts to produce more precise detailed information delivering high pixel density to generate a high-resolution image. Experimental results demonstrate high quality recovery of the image with better quantitative measures.

This paper introduces the theory of super-resolution image reconstruction and degraded model in brief, and presents a new super-resolution image reconstruction algorithm .The algorithm bases on the new image registration excluded aliased frequency domain and the Projection Onto Convex Set (POCS) method. The algorithm can precisely estimate the image registration parameter by excluding aliased frequency domain of the low-resolution images and killing the center part of the magnitude spectrum. In order to compute the shifts and the rotation angle, we set up the polar coordinates in the center of the image. By computing the frequency function of the rotation angle by integrating over radial lines, the algorithm converts the two-dimension correlation to one-dimension correlation. And then, the POCS method is used to reconstruct high-resolution image from these aliased image sequences. As a result, we find that the reconstruction algorithm has the same precision of image registration as the spatial image registration and good effect of super-resolution image reconstruction.KeywordsImage RegistrationRegistration AlgorithmTukey WindowIterative Back ProjectionAlias ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Coping with the limited spatial resolution of images, which is caused by the physical limitations of image sensors, is the objective of this thesis. Initially, an effort to model the scanner function when generating a document copy by means of simple models is made. In a task of scanner function simulation the proposed model should be preferred over the Gaussian and Cauchy models met in bibliography as it is equivalent in performance, simpler in implementation and does not present any dependence on certain scanner characteristics. Afterwards, new methods for improving images spatial resolution are formulated. A nonuniform interpolation method for Super-Resolution (SR) image reconstruction is proposed. Experimentation proves that the proposed method employing Kriging interpolation predominates over the method which creates the high-resolution grid by means of the weighted nearest neighbor interpolation that is a conventional interpolation technique. Also, three new methods for stochastic regularized SR image reconstruction are presented. The Tukey error norm in combination with the Bilateral Total Variation (BTV) regularization, the Lorentzian error norm in combination with the BTV regularization and the Huber error norm combined with the BTV regularization are the three proposed methods. An additional novelty is the direct comparison of the three estimators Tukey, Lorentzian and Huber in the task of super-resolution image reconstruction, thus in rejecting outliers. The performance of the proposed methods proves superior to that of a regularized SR technique met in bibliography. Experimental results verify the robust statistics theory. Moreover, a novel study which considers the effect of each one of the data-fidelity and regularization terms on the SR image reconstruction result is carried out. The conclusions reached help to select an effective SR image reconstruction method, among several potential ones, for a given low-resolution sequence of frames. Finally, an image interpolation method employing a neural network is proposed. The presented training procedure results in the network learning the scanner point spread function. Experimental results prove that the proposed technique predominates over the classical algorithms of bicubic and spline interpolation. The proposed method is novel as it treats, for the first time, the issue of the training data presentation order to the neural network input.

An implicit link adaptation technique based on hybrid automatic repeat request (H-ARQ) using a soft-combining algorithm is considered for transmission of Joint Photographic Experts Group 2000 (JPEG2000) images over wireless channels. Adaptation is carried out using an objective perceptual quality metric that takes into account the human perception. Retransmissions based on Region of Interest (ROI) are used to efficiently utilize the bandwidth. Numerical results shows that the use of the proposed reduced-reference hybrid image quality metric (RR-HIQM) and ROI in link adaptation provides robust link performance while meeting saticfactory quality constraints. Keywords : link adaptation, objective perceptual image quality metric, JPEG2000, region of interest

Super-resolution image reconstruction of a neutron thick pinhole imaging system using image denoiser prior based on half quadratic splitting method

Visual impairment has various forms all of which negatively affect the patient's daily activities and prevent performing simple actions like walking safely in a street. Content-aware image retargeting can be used to enhance the scene for patients who have limited visual field i.e. tunnel vision. A modified Seam Carving method is presented in this research paper which can decrease the width of the input image to fit in the patient's angle of vision while preserving the important objects in the original image as well as the image details. The method enhanced the original Seam Carving by calculating the energy map using multiscale image fusion that combines depth, saliency, foreground segmentation, and edge detection features, and used a forward-middle approach for the seam removal step. The results showed efficiency that outperformed various retargeting methods, achieving a 30.8% improvement in the composite score that integrates structural, perceptual, and feature-based quality metrics. Statistical analysis using paired t-tests ([Formula: see text]) confirmed statistically significant improvements across all major metrics ([Formula: see text]), including SSIM, SIFT feature matching, and modern deep learning-based perceptual quality metrics, compared to the baseline seam carving method.

Super Resolution (SR) is an interesting topic in video and image research. Super resolution image reconstruction (SRR) is one of the commonest SR technologies and has been proposed in many forms. SRR reconstructs a highresolution image from multiple low-resolution images. However, the low-resolution images require an aliasing process prior to reconstruction. The present paper analyzes SRR in the frequency domain, and demonstrates that SRR is unsuitable for general applications.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Sparse representation is one of the effective approaches to perform the feature extraction on the high-dimensional signals and has been widely used in the field of image processing. In this paper, a novel single image super-resolution (SR) reconstruction algorithm based on sparse coding is presented. Specifically, due to the fact that sparse coding has the potential to greatly facilitate the biological-neural system and to deal with a large amount of complex data, while consuming a finite amount of energy, a memristor crossbar array synthesized by a threshold-type memristor model is constructed for the implementation of a hardware-friendly sparse coding method, i.e., the Locally Competitive Algorithm (LCA). Meanwhile, the relevant online dictionary learning method is provided, along with the description of weight programming strategy. Furthermore, the specific description of the image SR via the LCA sparse coding is provided. For the verification purpose, a series of experiments are carried out to illustrate the validity and effectiveness of the entire scheme.

Social media network is inseparable from image recognition, and image super-resolution (SR) reconstruction plays an important role in image recognition. The changes of scale and geometry are rarely considered in the image super-resolution reconstruction based on deep learning over the years, we introduce a super-resolution reconstruction network based on deformable convolutional network. We replace the ordinary convolution with the deformable convolution to pretend the geometric deformation and extract abundant local features. The image super-resolution reconstruction is usually based on the conventional convolutional neural network (CNN). Most CNN-based SR models do not utilize the features of the original low resolution (LR) image as much as possible, resulting in lower performance. After introducing the idea of deformable convolution, though the complexity is increased, the recognition accuracy is obviously raised.

Recent advances in deep convolutional neural network (CNN) based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8 x 10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid cell areas, super resolve log-normally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high resolution simulations and ensembles at a fraction of the computational cost.

Recent advances in deep convolutional neural network (CNN) based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8 x 10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid cell areas, super resolve log-normally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high resolution simulations and ensembles at a fraction of the computational cost.

In this paper, trade-off between effect of registration error and number of images used in the process of super resolution image reconstruction is studied. Super Resolution image reconstruction is three phase process, of which registration is of at most importance. Super resolution image reconstruction uses set of low resolution images to reconstruct high resolution image during registration. The study demonstrates the effects of registration error and benefit of more number of low resolution images on the quality of reconstructed image. Study reveals that the registration error degrades the reconstructed image and without better registration methodology, a better super resolution method is still not of any use. It is noticed that without further improvement in the registration technique, not much improvement can be achieved by increasing number of input low resolution images.