A Novel approach to super resolution image reconstruction algorithm from low resolution panchromatic images

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.

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

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

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.

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.

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.

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.

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.

Superresolution (SR) image reconstruction is able to overcome the resolution limit of camera imaging system through integrating information of multiple low resolution (LR) images that the perceived resolution of the reconstruction image is much higher than that of the individual image. Since the ill-posed SR reconstruction problem can be regularized within a Bayesian context by adopting a priori image model, we propose a hybrid Bayesian method for image reconstruction, which firstly estimates the unknown point spread function (PSF) and an approximation for the original ideal image, and then sets up the Huber Markov Random Field (HMRF) image prior model and assesses its tuning parameter using maximum likelihood (ML) estimator, finally computes the regularized solution automatically. Hybrid Bayesian estimates computed on simulation images, satellite images and video sequence show dramatic visual and quantitative improvements over bilinear interpolation and Maximum A Posteriori reconstruction results with sharp edges, correctly restored textures and a high PSNR improvement.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.

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

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.

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.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a two-stage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional many-to-many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

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.