Super-resolution terahertz synthetic aperture image reconstruction algorithm.

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

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.

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.

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.

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.

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.

The image degradation caused by motion blur, non-ideal sample and noise was producing in the process of Image acquirement. This paper proposed a fast super-resolution image reconstruction algorithm basing on image sequences. On the basis of image registration, registration algorithm used affine transform as geometric transform Model. A sequence of low-resolution images was roughly registered basing on feature and then use registration algorithm basing on Gray to optimize the result. Iterative back-projection technique was used to construct high resolution from image sequences. Firstly it made common low-resolution sequence images relate to standard displacements, and then reconstructed high-resolution image according to the relationship between low-resolution sequence images with standard displacement and high-resolution image. The high-frequency was distilled through the local estimation. By compensating the high-frequency component, the high-resolution images were recovered. Experimental results show that this algorithm solve the problem that the translation and rotation is small in traditional method. It has characterized over low computation complexity, fast convergence. The details, definition and resolution of high resolution image processed with the proposed method are effectively improved.

Blind image super-resolution reconstruction is one of the hot and difficult problem in image processing. a framework of blind single-image super-resolution reconstruction algorithm is proposed. In the low-resolution imaging model, the processes of motion blur, down-sampling, and noise are considered. The parameter of motion blur is estimated through an error-parameter analysis method. Utilizing Wiener filtering image restoration algorithm, an error-parameter curve at different motion distance is generated, from which the motion distance of the motion point spread function (PSF) can be estimated approximately. The super-resolution image is reconstructed through the iterative back projection (IBP) algorithm. The experimental results show that motion PSF estimation plays an important role on the quality of the SR reconstructed image, and also demonstrate the effectiveness of the proposed algorithm.

Contribution of a new super-resolution deep-learning image reconstruction algorithm coupled with an extended matrix to cardiac CT image quality.

Development of a Super-resolution Panoramic X-ray Image Reconstruction Algorithm Using the DEMOT Method

A camera shake of the relative motion between cameras and original scenes often occurs during a shoot, resulting in blurred images. This paper presents a method of restoring blurred images based on patch-based PSF (Point Spread Function) estimation and channel-dependent image deblurring. Initially, a pair of noisy and blurred images is acquired by consecutive shooting under short and long exposures to more accurately estimate the 2D trajectory formed by the camera shake, which is also known as PSF. From the noisy image, one patch with strong edges is extracted using gradient operators and the corresponding blurry patch is found within the blurry image. The boundaries of both patches are padded in a specified tile pattern to prevent the appearance of vertical or horizontal lines due to boundary discontinuity. Tikhonov regularization is then iteratively conducted on Gaussian pyramid images with adaptive noise thresholding to get the optimized PSF. Finally, the color conversion of the blurred image into a luminance and two chrominance images is done to apply the channel dependent image deblurring where the spatial resolution of the chrominance images is reduced based on the natural gradient distribution to abandon the valueless data regardless of the restoration quality. Moreover, another artificially blurred image made by the convolution of the known Gaussian PSF and the noisy image is used to reduce undesirable ringing-artifacts and increase the sharpness. The experimental results show that the proposed method can provide more improved PSFs and restored images accompanied by the reduction of computing time and ring artifacts.

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

High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery.

Images of high-resolution are desired and often required in most photoelectronic imaging applications, and corresponding image reconstruction algorithm has became the frontier topics. On the basis of stochastic theory, a novel super-resolution image reconstruction algorithm based on Tukey norm data fusion and bilateral total variation regularization is proposed in this paper. The Tukey norm is employed for fusing the data of low-resolution frames and removing outliers in the data, and then aiming at the sickness of super-resolution reconstruction, the bilateral total variation regularization as a priori knowledge about the solution is incorporated to remove the artifacts from the final answer and improve the convergence rate. Simulated and real experiment results show that the proposed algorithm can improve the image resolution greatly and it is immune to noise and errors in motion and blur estimation.

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.