POCS Super-Resolution Sequence Image Reconstruction Based on Image Registration Excluded Aliased Frequency Domain

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.

Super-resolution (SR) reconstruction, particularly on face images, can be widely used in forensic analysis and video surveillance. In this paper, we investigate the statistical characteristics of face images and incorporate them into SR reconstruction in terms of deterministic sets. Based on the set theoretic formulation, the projection onto convex sets (POCS) algorithm is applied to find the solution to face image reconstruction. Compared with the traditional POCS based SR methods, the proposed approach imposes additional constraints to the solution. The experimental results on frontal face images show that the proposed approach gains a better performance both on noise suppression and reconstruction quality.

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

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.

Medical image super-resolution reconstruction algorithms based on deep learning: A survey

Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. Super-resolution is the process of combining multiple aliased low-quality images to produce a high resolution, high-quality image. The problem of recovering a high resolution image progressively from a sequence of low resolution compressed images is considered. In this paper we propose a novel DCT based progressive image display algorithm by stressing on the encoding and decoding process. At the encoder we consider a set of low resolution images which are corrupted by additive white Gaussian noise and motion blur. The low resolution images are compressed using 8 by 8 blocks DCT and noise is filtered using our proposed novel zonal filter. Multiframe fusion is performed in order to obtain a single noise free image. At the decoder the image is reconstructed progressively by transmitting the coarser image first followed by the detail image. And finally a super resolution image is reconstructed by applying our proposed novel adaptive interpolation technique. We have performed both objective and subjective analysis of the reconstructed image, and the resultant image has better super resolution factor, and a higher ISNR and PSNR. A comparative study done with Iterative Back Projection (IBP) and Projection on to Convex Sets (POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that our method has out performed the previous contributions.

摘要: 图像配准是超分辨率重建中的一个关键问题, 直接影响超分辨率重建图像的质量. 本文在自适应十字搜索(Adaptive rood pattern search, ARPS)块匹配算法的基础上, 根据小波域中各图像之间的相关性, 提出一种分层块匹配算法---基于小波变换的改进的自适应十字模式搜索算法(Improved adaptive rood pattern search algorithm based on wavelet transform, W-IARPS), 该方法在小波变换域完成匹配宏块的搜索, 有效地减少了匹配点的搜索个数, 且配准图像的峰值信噪比相比全搜索算法下降不到0.l dB, 保持了较高的配准精度. 最后采用凸集投影(Projections onto convex sets, POCS)算法对配准后的图像进行超分辨率重建, 取得了较好的视觉效果. 实验结果表明, 该方法具有较高的配准精度和重建效果, 算法稳健可靠. 关键词: 超分辨率 / 块匹配 / 图像重建 / 小波变换 / 凸集投影

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

Aiming at the disadvantages of the traditional projection onto convex sets of blurry edges and lack of image details, this paper proposes an improved projection onto convex sets (POCS) method to enhance the quality of image super-resolution reconstruction (SRR). In traditional POCS method, bilinear interpolation easily blurs the image. In order to improve the initial estimation of high-resolution image (HRI) during reconstruction of POCS algorithm, the initial estimation of HRI is obtained through iterative curvature-based interpolation (ICBI) instead of bilinear interpolation. Compared with the traditional POCS algorithm, the experimental results in subjective evaluation and objective evaluation demonstrate the effectiveness of the proposed method. The visual effect is improved significantly and image detail information is preserved better.

For polarized remote sensors, the polarization images of ground objects acquired at different spatial scales will be different due to the spatial heterogeneity of the ground object targets and the limitation of imaging resolution. In this paper, the quantitative inversion problem of a typical polarized remote sensor at different spatial scales was studied. Firstly, the surface roughness of coatings was inversed based on the polarized bidirectional reflectance distribution function (pBRDF) model according to their polarization images at different distances. A linear-mixed pixel model was used to make a preliminary correction of the spatial scale effect. Secondly, the super-resolution image reconstruction of the polarization imager was realized based on the projection onto convex sets (POCS) method. Then, images with different resolutions at a fixed distance were obtained by utilizing this super-resolution image reconstruction method and the optimal spatial scale under the scene can be acquired by using information entropy as an evaluation indicator. Finally, the experimental results showed that the roughness inversion of coatings has the highest accuracy in the optimal spatial scale. It has been proved that our proposed method can provide a reliable way to reduce the spatial effect of the polarized remote sensor and to improve the inversion accuracy.

The resolution of terahertz images based on the near-field synthetic aperture radar (NSAR) imaging mode is low due to the narrow antenna beamwidth and the electromagnetic wave loss that was ignored by previous algorithms. We propose a super-resolution terahertz NSAR imaging algorithm. There are two algorithm steps: First, we use the forward mathematical model of the NSAR imaging system and the back-projection algorithm (BPA) to calculate the point spread function (PSF). The next step is to deconvolve the NSAR image reconstructed by BPA to enhance its lateral resolution. To evaluate the effectiveness of our proposed method, we conducted both simulations and experiments. The experiment results show that the proposed method achieves a resolution of 0.67λ, which is a significant improvement. Overall, the proposed method has great potential to enhance the resolution of THz images and advance the use of THz technology in various fields.

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.

The topic of super-resolution image reconstruction has recently received considerable attention within the research area. Super-resolution image reconstruction methods attempt to create a single high-resolution image from a number of low-resolution images (or a video sequence). The Bayesian maximum a posteriori (MAP) estimation method is widely used for super-resolution image reconstruction. So far, existing super-resolution reconstruction methods are all confronted with the problem of slow convergence and expensive computation. To satisfy the requirement of real-time application, we propose a high-efficiency super-resolution reconstruction algorithm that solves two key bottlenecks in the multi-frame MAP framework. The first breakthrough is to select the Armijo rule to identify the step length instead of the exact line search. The second one is to approximately compute the gradient of the MAP objective function using analytic representation instead of numerical calculation. Experimental results show that the proposed algorithm is applicable to real-time super-resolution reconstruction of low-resolution image sequences.

Compared with traditional contact precision measurement, vision-based non-contact precision measurement has the features of low cost and flexible multi-point information extraction, but how to ensure the measurement accuracy of vision-based non-contact precision measurement is an urgent problem. Traditional thinking often focuses on hardware upgrades to improve image resolution, but this brings high costs and is limited by the physical characteristics of the hardware itself. In this paper, we start from the software aspect to improve the image resolution by using the super-resolution reconstruction algorithm and propose an image super-resolution reconstruction algorithm—Swin Transformer with a Vast-receptive-field Pixel Attention, which combines the vast-receptive-field pixel attention mechanism with the Swin Transformer self-attention mechanism, focuses on the learning of the high-frequency information features of the image. Experiments are conducted both in public datasets and real measurement images. Extensive experimental validation shows that the model can obtain more edge and high-frequency detail features in public datasets, and the objective evaluation index on Set5, Set14, B100, Urban100, and Manga109 datasets is improved by 0.06 dB on average compared with the existing algorithms. In actual measurements, the algorithm in this paper for USAF1951 resolution tablet, image super-resolution reconstruction image in the horizontal and vertical direction of the measurement accuracy increased by an average of 6.97%, the horizontal and vertical direction of the relative measurement accuracy of an average of 30.20% improvement. This study provides a potential development direction for vision-based non-contact precision measurement.

In domains such as medical diagnostics, surveillance technology, and geospatial imaging, the escalating need for ultra-high-definition imagery has exposed the limitations of conventional super-resolution methods. These legacy algorithms often fail to deliver the precision and clarity demanded by modern applications. Therefore, this article proposes an optimization algorithm based on the AWSRN network model, aiming to achieve efficient image superresolution reconstruction, reduce computational costs, and enhance image realism. Firstly, optimize the internal structure of the network and enhance its feature extraction and fusion capabilities; Secondly, to enhance feature extraction precision, a novel module integrating depth-separable convolution with an attention-based mechanism is proposed. Additionally, a hybrid loss function-merging perceptual quality metrics with adversarial training objectives-is employed to rigorously evaluate the disparity between generated and groundtruth images. The MPTS training strategy further optimizes convergence efficiency. Empirical evaluations demonstrate that the enhanced AWSRN model achieves substantial improvements over its baseline counterpart across multiple upscaling factors, particularly at 4 × magnification. Specifically, on the Urban100 benchmark, the proposed method elevates PSNR by 1.06 points and SSIM by 0.0239, while maintaining computational efficiency. These advancements offer valuable insights for high-fidelity image upscaling methodologies.