Initial High Resolution Image Research Articles

Lucy Richardson and Mean Modified Wiener Filter for Construction of Super-Resolution Image

The ultimate goal of the Super-Resolution (SR) technique is to generate the High-Resolution (HR) image by combining the corresponding images with Low-Resolution (LR), which is utilized for different applications such as surveillance, remote sensing, medical diagnosis, etc. The original HR image may be corrupted due to various causes such as warping, blurring, and noise addition. SR image reconstruction methods are frequently plagued by obtrusive restorative artifacts such as noise, stair casing effect, and blurring. Thus, striking a balance between smoothness and edge retention is never easy. By enhancing the visual information and autonomous machine perception, this work presented research to improve the effectiveness of SR image reconstruction The reference image is obtained from DIV2K and BSD 100 dataset, these reference LR image is converted as composed LR image using the proposed Lucy Richardson and Modified Mean Wiener (LR-MMWF) Filters. The possessed LR image is provided as input for the stage of bicubic interpolation. Afterward, the initial HR image is obtained as output from the interpolation stage which is given as input for the SR model consisting of fidelity term to decrease residual between the projected HR image and detected LR image. At last, a model based on Bilateral Total Variation (BTV) prior is utilized to improve the stability of the HR image by refining the quality of the image. The results obtained from the performance analysis show that the proposed LR-MMW filter attained better PSNR and Structural Similarity (SSIM) than the existing filters. The results obtained from the experiments show that the proposed LR-MMW filter achieved better performance and provides a higher PSNR value of 31.65dB whereas the Filter-Net and 1D,2D CNN filter achieved PSNR values of 28.95dB and 31.63dB respectively.

Online Education Classroom Intelligent Management System Based on Tensor CS Reconstruction Model

To study a high-efficiency online classroom intelligent management system, this article builds an artificial intelligence classroom management system based on the tensor CS reconstruction model. Moreover, this study uses the cosine function to represent the data energy fitting of the traditional active contour model and proposes a local cosine fitting energy active contour model based on partial image restoration, which is used for image and composite image segmentation. Simultaneously, this study proposes a new type of super-resolution algorithm. This algorithm performs Fourier transform of a low-resolution image into a frequency range and then performs an inverse Fourier transform on the image expanded in the frequency range to obtain the initial high-resolution image and finally reconstructs a new super-resolution image using the frequency-domain compressed data of the high-resolution image. Finally, this study verifies and analyzes the performance of the model through experiments. The research results are basically consistent with the expectations of the model.

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

Single image super-resolution using feature adaptive learning and global structure sparsity

Due to the important application value of image super-resolution, many image super-resolution algorithms have been proposed in recent years. However, many single-image super-resolution algorithms usually have their own limitations and cannot achieve ideal results. To this end, this paper proposes a new single-image super-resolution method that uses non-local self-similarity, cross-resolution similarity, and global structure sparsity without relying on external instances. First, we obtain the initial high-resolution image through feature-constrained polynomial interpolation. Then, we use a database built by the input image to perform cross-resolution learning to predict the missing high-frequency information in the image. Finally, we use the residual filtering proposed in this paper to remove the noise introduced during interpolation and cross-resolution learning. Our method can be combined with other image super-resolution algorithms. Through extensive comparison experiments to verify, our method achieves higher numerical accuracy and pleasing visual effects.

Innovation of English teaching model based on machine learning neural network and image super resolution

At present, applying image recognition technology to promote English teaching is a kind of teaching innovation that meets the needs of the times. Therefore, based on machine learning neural network and image super-resolution, this study conducted an innovative analysis of English teaching mode. This paper combined the current situation of English teaching classroom to study and analyze English classroom, combined classroom characteristics as the basis of English teaching innovation and constructed a feature recognition model suitable for current English teaching status. Moreover, this paper formed an initial high-resolution image for low-resolution image reconstruction by sparse representation method, and then established a mixed sample spine regression model to re-estimate the high-frequency components of the initial high-resolution image to realize various behavioral characteristics of students in English teaching classroom. In addition, this article builds a verification test. The research shows that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution

Recently, various deep-neural-network (DNN)-based approaches have been proposed for single-image super-resolution (SISR). Despite their promising results on major structure regions such as edges and lines, they still suffer from limited performance on texture regions that consist of very complex and fine patterns. This is because, during the acquisition of a low-resolution (LR) image via down-sampling, these regions lose most of the high frequency information necessary to represent the texture details. In this paper, we present a novel texture enhancement framework for SISR to effectively improve the spatial resolution in the texture regions as well as edges and lines. We call our method, high-resolution (HR) style transfer algorithm. Our framework consists of three steps: (i) generate an initial HR image from an interpolated LR image via an SISR algorithm, (ii) generate an HR style image from the initial HR image via down-scaling and tiling, and (iii) combine the HR style image with the initial HR image via a customized style transfer algorithm. Here, the HR style image is obtained by down-scaling the initial HR image and then repetitively tiling it into an image of the same size as the HR image. This down-scaling and tiling process comes from the idea that texture regions are often composed of small regions that similar in appearance albeit sometimes different in scale. This process creates an HR style image that is rich in details, which can be used to restore high-frequency texture details back into the initial HR image via the style transfer algorithm. Experimental results on a number of texture datasets show that our proposed HR style transfer algorithm provides more visually pleasing results compared with competitive methods.

Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior

Medical images have high information redundancy, which can be used to improve image analysis and visualization for purpose of healthcare. In order to recover a high-resolution (HR) image from its low-resolution (LR) counterpart, this paper proposes a resolution enhancement method by using the nonlocal self-similar redundancy and the low-rank prior. The proposed method consists of three main steps. First, an initial HR image is generated by nonlocal interpolation, which is based on the self-similarity of medical images. Next, the low-rank minimum variance estimator is exploited to reconstruct the HR image. At last, we iteratively apply the subsampling consistency constraint and perform the low-rank reconstruction to refine the reconstructed HR result. Experimental results conducted on MR and CT images demonstrate that the proposed method outperforms conventional interpolation methods and is competitive with the current stat-of-the-art methods in terms of both quantitative metrics and visual quality.

Non‐local feature back‐projection for image super‐resolution

Image super-resolution (SR) for a single low-resolution image is an important and challenging task in image processing. In this study, the authors propose a novel non-local feature back-projection method for image SR, which can effectively reduce jaggy and ringing artefacts common, in general, iterative back-projection (IBP) method. In their method, the objective high-resolution (HR) image is obtained by projecting reconstructed errors back to HR image iteratively. To optimise the initial HR image and constrain anisotropic errors propagation during IBP process, an efficient non-local feature interpolation algorithm is designed. Specially, edge information is used as constraints to make the interpolation surface preserve better shape. Furthermore, as post-processing, non-local similarities are utilised to remove noise and irregularities induced by errors propagation. Experimental results show that their method achieves better performance than state-of-the-art methods in terms of both quantitative metrics and visual qualities.

Image interpolation based on nonlocal self-similarity

This paper presents an efficient method for the resolution enhancement of low resolution (LR) images. Inspired by the great success of nonlocal filtering, the proposed method interpolates a missing pixel by a nonlocal estimation which takes into account the self-similarity of images and the geometric duality between LR images and high resolution (HR) images. This method firstly exploits the SAI method to generate an initial HR image of the given LR image. Then the initial HR image is further improved by a nonlocal estimation that uses the given LR image as a guiding image. Pixel correction is applied to reduce the distortion of the HR estimate. Experimental results demonstrate that the proposed method provides competitive performance.

Multi-frame image super-resolution reconstruction using sparse co-occurrence prior and sub-pixel registration

In this paper, a hybrid super-resolution (SR) method is proposed by combining the concepts of both multi-frame and single-frame SR to generate a high-resolution (HR) image. The main contributions are in two aspects: the first one is hierarchical iterative sub-pixel registration, which provides accurate registration information of input low-resolution (LR) images or frames, to generate an initial HR image; the second one is to enhance the initial HR image with sparse co-occurrence prior, resulted from specially-designed dictionaries containing patches from both generic training images and interpolated input LR images. As a whole, the proposed hybrid SR method makes use of information from both sub-pixel registration and sparse co-occurrence prior to get reconstructed SR image with large zoom-in factor. The simulation results from synthetic images and real video frames illustrate its effectiveness and the superiority in image quality over conventional multi-frame and single-frame SR methods.

High resolution image formation from low resolution frames using delaunay triangulation

An algorithm based on spatial tessellation and approximation of each triangle patch in the Delaunay (1934) triangulation (with smoothness constraints) by a bivariate polynomial is advanced to construct a high resolution (HR) high quality image from a set of low resolution (LR) frames. The high resolution algorithm is accompanied by a site-insertion algorithm for update of the initial HR image with the availability of more LR frames till the desired image quality is attained. This algorithm, followed by post filtering, is suitable for real-time image sequence processing because of the fast expected (average) time construction of Delaunay triangulation and the local update feature.