Through distinction between noise and signal subspaces in an observation matrix, this paper shows that a 38-fold improvement in the full width at half maximum (FWHM) estimation of IFPI optical path Super-resolution image reconstruction refers to the reconstruction of a clear (or multiple) high-resolution image from a low-resolution degraded image (or image sequence) of the same scene. It runs at 100 milliseconds per 12-megapixel RAW input burst frame on mass-produced mobile phones. In comparison to conventional methods, our approach has demonstrated Peak Signal-to-Noise Ratio (PSNR), Structural Oct 13, 2023 · Then, a pretrained VSR algorithm is used to upscale the resolution of these low-definition videos, resulting in super-resolution videos. Numerous studies have demonstrated the Swin Transformer performs well in image super-resolution tasks. The Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model is improved in terms of both Feb 2, 2021 · The traditional super resolution restoration algorithm generates one high resolution image by using multi frame low resolution image sequences in time 3. 1 Single Image Super Resolution. In summary, the super-resolution lensless camera provides users with additional options Oct 20, 2005 · The Richardson-Lucy deconvolution (RL deconvolution) algorithm achieves both image restoration and super-resolution, where super-resolution refers to the restoration of high-frequency components Super-Resolution. This paper evaluates the performance of different algorithms by combining subjective and objective evaluations (including super-resolution video quality and VSR computational overhead). However, most of the existing SR models suffer from the insufficient utilization of spectral information, limiting their SR performance. However, in the region with rich texture details, the super-resolution results generated by the existing By Anil Chandra Naidu Matcha. Oct 19, 2023 · The proposed method is experimented on Chang'e-2 7-meter resolution lunar surface remote sensing images, which verifies the effectiveness of the super-resolution algorithm proposed in this paper and outperforms the current popular methods in terms of visual effects and commonly used evaluation metrics. May 8, 2019 · Our algorithm is robust to challenging scene conditions: local motion, occlusion, or scene changes. The original face image and the super-resolution images by using different algorithms( × 3). There are two problems by using this Sep 1, 2017 · The proposed a volumetric super resolution method which can enhance the resolution in all directions for 3D volumetric CT images of reservoir rocks. Paper. Then, the high frequency sub images of the original low-resolution image are interpolated in the wavelet transform domain, and the high-resolution image is obtained by the bicubic 1. When any two atoms are less than 1/M apart, this recovery problem is highly challenging and many existing algorithms either cannot deal with this situation or require restrictive assumptions on the sign Nov 19, 2012 · For the last 15 years, super-resolution (SR) algorithms have successfully been applied to magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after acquisition has been performed, thus facilitating the doctors' diagnosis. Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. Super-resolution Nov 5, 2023 · Table 2 shows the PSNR and SSIM of the image super-resolution reconstruction algorithm under different conditions. 3 (or greater) installed on your system. 271 , 266–281 (2018). It forms the basis of most computer vision and image processing models. In recent years, adversarial network-based research has made great progress in single-image super-resolution, In particular, the Enhanced Super-Resolution Generative adversarial networks (ESRGAN) can generate realistic and natural high-resolution images from low-resolution images. In this sub-section, we will theoretically review on the recent state-of-the-art algorithms for the task of the image super-resolution. High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. To overcome the problem, we present here a new deep learning-based algorithm that extends the well-known Deep Image Prior (DIP) to TLM Video Super Resolution without requiring any training. Aiming at the problem of face image restoration, a super resolution image restoration algorithm based on wavelet and bicubic interpolation is proposed. Jan 1, 2020 · C. If the low-resolut Patrick Vandewalle, Sabine Süsstrunk and Martin Vetterli. In particular, following recent developments, we impose this external Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. Image super-resolution (SR) is the process of recovering high-resolution (HR) images from low-resolution (LR) images. This technology has become a research hotspot in the field of image processing. In [8], the Chen’s group proposed the novel low-rank neighbor embedding for single image super-resolution. So, this article proposes an improved Tikhonov regularization direction total variation (DTV) deconvolution super-resolution algorithm based on Rayleigh entropy. In response to the current image super-resolution reconstruction algorithm still suffers from insufficient detail processing such as texture and artifacts, this paper proposes an image super-resolution algorithm based on an improved enhanced super-resolution generative adversarial network. Low-resolution image is decomposed by wavelet transform. In the field of image super-resolution reconstruction, more and more methods based on deep learning have been studied Jul 4, 2022 · Super-resolution (SR) implies the conversion of an image from a lower resolution (LR) to images with a higher resolution (HR). One of famous and widely used the image formulation model is the observation model. MUltiple SIgnal Classification (MUSIC) is a high-resolution direction-finding algorithm based on the eigenvalue decomposition of the sensor covariance matrix observed at an array. A sought-after super-resolution algorithm can enhance Raman interference pattern images, reducing reliance on expensive imaging sensors. This paper considers the performance of a robust super resolution (SR) algorithm on simulated infrared images containing scenes with very different textures-corresponding to different geographical textures (urban and rural). We introduce, to the best of our knowledge, a new 3D reconstruction algorithm based on the Richardson-Lucy deconvolution. In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. This algorithm consists of two parts: registration and reconstruction. In recent years, with the vigorous development of deep learning, this technology has been widely used in many fields. Luckily, OpenCV 4. Nov 9, 2020 · In order to apply OpenCV super resolution, you must have OpenCV 4. The method improves edge recognition through fractional differential and solves the problem of missing data in traditional super-resolution algorithms for depth images. Jul 12, 2023 · Regarding the SDR algorithm, JSFR-SIM provides a resolution improvement effect comparable to OpenSIM, but it cannot suppress defocus information as effectively as HiFi-SIM. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. 6 Qualitative Performance Evaluation Oct 1, 2021 · The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. However, available image super-resolution methods, espe-cially deep learning methods, are mostly Sep 24, 2022 · 2. However, during my testing I found it much faster than Gigapixel AI. Feb 1, 2019 · Three-dimensional structured illumination microscopy (3D-SIM) is a wide-field super-resolution technique in fluorescent imaging that can double the resolution beyond its classical limit. 4. Firstly, the performance of the SR images produced by ESRNet is evaluated by comparing it to those generated by five other SR methods. An image matrix size of 512 × 512 has been the standard for almost all clinical CT applications. modeled a SIQA metric based on visual perception . Single image super-resolution (SISR) is an important research field in computer vision, the purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) images. Multi-Frame Super-Resolution fuses these low-res inputs into a composite high-res image that can reveal some of the original detail This paper describes a deep learning-based self-supervised anti-aliasing (SAA) and self-supervised super-resolution (SSR) algorithm SMORE. Oct 8, 2019 · To improve the resolution from such anisotropic acquisitions, researchers have developed many super-resolution algorithms. The algorithm introduces the DTV operator to more accurately restore the edge texture details of the image, and adds a weight matrix to the loss function to more accurately reflect the Jan 7, 2021 · The super-resolution reconstruction of X-ray images is one of the hot issues in the field of medical imaging. Mar 22, 2021 · Further, super resolution is present inside the module dnn_superres (Deep Neural Network based Super Resolution) which was implemented in OpenCV version 4. Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). Accurate Jul 4, 2022 · Zhao et al. Specifically, the SR performance is To address the limitations of traditional approaches in super-resolution reconstruction of medical oral images, we have devised a novel method for medical oral image super-resolution reconstruction using a stable diffusion model called Stable Oral Reconstruction Technique (SORT). This paper discusses the performance of EDSR, one of the best SISR algorithms, under different conditions. 11 investigated modified algorithms to allow sparse super-resolution. With the rapid development of deep learning technology in natural language processing, computer vision and other fields, it has attracted extensive attention from industry and academia. There are two algorithm steps: First, we use the forward mathematical model of the NSAR imaging system and the We also consider the question of super-resolution performance, which is the measure of how much lost bandwidth can be recovered from a super-resolution algorithm, and how the performance is related to the algorithm principles that allow super-resolution to occur. It is popularly used in the following applications: Surveillance: to detect, identify, and perform facial recognition on low-resolution images obtained from security cameras. However, it is difficult to break through the traditional method. We conclude with examples of super-resolution. However, targets are often closer than the maximum detectable distance. Enhancing the spatial resolution of an image is an important field of research in number of applications including medical ones. To address this problem To address this issue, a super-resolution (SR) network called ESRNet has been developed to enhance the quality of OCT images, facilitating their applications in research. In recent years, with the vigorous Feb 27, 2019 · Comparison of PBSR with other super resolution methods. The pixel-level differences between each of the frames in the burst can be used to merge several images of the ABSTRACT Image super-resolution reconstruction uses a specific algorithm to restore the low resolution blurred image in the same scene to a high resolution image. Because the dependence between frames is complex, traditional methods lack the ability to model the complex dependence, and it is difficult to estimate and compensate the motion accurately in the process of video super-resolution reconstruction. We 136 papers with code • 15 benchmarks • 15 datasets. evaluated visual quality of SR images subjectively with stat-of-the-art single image super-resolution algorithms . Furthermore, many deep learning super-resolution technologies face the challenge of computationally intensive A novel truly 3D super-resolution algorithm has been conceived, implemented, and tested. It covers the criteria to choose different fluorophores for various SRM methods and critically assesses the nitty Mar 1, 2023 · In the past few years, there has been a significant advancement in image super-resolution algorithms. Recent high-spatial-resolution CT scanners have spatial resolutions of up to 150 µm. Section 4 Image super-resolution reconstruction uses a specific algorithm to restore the low resolution blurred image in the same scene to a high resolution image. Finally combine these subspaces back to get the high resolution image. Feb 18, 2021 · A Comprehensive Review of Deep Learning-based Single Image Super-resolution. a, Schematic of super-resolution SRS image processing. This paper aims to provide the detailed survey on recent advancements in image super-resolution in terms of traditional, deep learning and the latest transformer-based algorithms. Precise alignment of the input images is an essential part of such algorithms. Abstract. It can be seen from Table 2 that when the resolution is enlarged by 4 times and 8 times, the proposed method is superior to other network models in both PSNR and SSIM image Apr 29, 2024 · The way forward. 3 for Python. Super resolution uses machine learning techniques to upscale images in a fraction of a second. EURASIP Journal on Advances in Signal Processing 2006 2006 :071459. As Abstract: Single-Image Super-Resolution (SISR) is a technology to enhance the resolution of images. Jul 24, 2023 · In recent years, the development of super-resolution (SR) algorithms based on convolutional neural networks has become an important topic in enhancing the resolution of multi-channel remote sensing images. b, Three-dimensional deconvolution result of LDs (2,850 cm −1) in a live cell. If you already have OpenCV installed, you can check its version using the following code snippet: import cv2. Sep 1, 2021 · This work structurally analyze the network structure of the algorithm and the commonly used loss functions and helps researchers to deeply understand the video super-resolution technology based on artificial intelligence. Performance of the PBSR algorithm was evaluated against two accepted super resolution methods: linear interpolation (LI) and image fusion (IF). The aim is to assess the usefulness of the robust SR technique for the detection of small objects in infrared imagery-in the presence of typical image noise and variations Sep 24, 2023 · High-resolution interference pattern images are vital for spatial heterodyne Raman spectroscopy to produce quality Raman spectra with a good signal-to-noise ratio. • The proposed volumetric super resolution method can enhance the resolution with only one input image without a series of low-resolution images of the same scene with sub-pixel shifts. In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. They first extract input low resolution to Aug 31, 2023 · A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. Nov 26, 2023 · Super Resolution is much slower than using traditional up-sizing algorithms. In their paper [3], Glasner et al. Dec 1, 2006 · Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. The camera can work beyond the theoretical diffraction limit and tremendously enhance the resolution. Image super-resolution reconstruction uses a specific algorithm to restore the low resolution blurred image in the same scene to a high resolution image. The goal is to produce an output image with a higher resolution than the input image, while Dec 14, 2023 · In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). In recent years, with the This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. Impressive results for image super-resolution algorithms based on convolutional neural networks. Syed Muhammad Arsalan Bashir, Yi Wang, Mahrukh Khan, Yilong Niu. 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. Therefore, a reconstruction Feb 1, 2019 · Request PDF | Super-resolution algorithm based on Richardson–Lucy deconvolution for three-dimensional structured illumination microscopy | Three-dimensional structured illumination microscopy Super-resolution algorithm for joint range-azimuth-Doppler estimation in automotive radars Abstract: The problem of localization of targets and determining their velocities by means of radar is formulated as a joint estimation problem over single, three-dimensional domain. Super Resolution would take well under a minute to up-size my 20-24 Megapixel files, where Gigapixel AI would take around 5 minutes on my powerful photo editing PC. However, targets are often closer than the maximum detectable Sep 13, 2022 · However, terahertz imaging has not received a satisfactory image quality so far due to problems such as complex noise, artifacts and low resolution. Due to SR’s ill-posed nature, many possible solutions exist. To solve the problem, this paper proposes an improved medical image super-resolution Unfortunately, due to physical and cost limitations, acquiring high resolution videos is not always possible. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video. It is an important class of image processing techniques in computer vision and image processing and enjoys a wide range of real-world applications, such as medical imaging, satellite imaging, surveillance and Feb 1, 2019 · This work introduces a new 3D reconstruction algorithm based on the Richardson-Lucy deconvolution that can double the resolution beyond its classical limit in 3D structured illumination microscopy and shows true optical sectioning and super-resolution can be achieved simultaneously. The standard resolution of this inverse problem is 1/M and super-resolution refers to the capability of resolving atoms at a higher resolution. Our algorithm consists of three steps: (1) We build a self AA (SAA) deep network followed by (2) an SSR deep Oct 16, 2023 · This algorithm seamlessly integrates graph regularization and tensor ring decomposition, presenting an innovative fusion model that effectively leverages the spatial structure and spectral information inherent in hyperspectral images to enhance the quality of super-resolution reconstruction and preserve image features. T. Section 3 describes the optimization process of the GRTR algorithm in detail. Following Jan 1, 2014 · To comprehensively analyse the super resolution algorithm, the definition of the relation between a high resolution image and several low resolution images is necessary. This paper studies the use of MUltiple SIgnal Classification (MUSIC) as a super-resolution algorithm to improve demodulation results for intrinsic Fabry–Perot interferometer (IFPI) sensor arrays. When multiple images of the same view are taken from slightly different positions, perhaps also at different times, then they collectively contain more information than any single image on its own. Unfortunately, such training data is often unavailable. Generally, CNN methods need paired atlas images to learn the transformation from low resolution (LR) to high resolution (HR). MUSIC belongs to the family of subspace-based direction-finding algorithms. We don’t expect perfect resolution independence—even the polygon represen-tation doesn’t have that—but increasing the resolution independence of pixel-based representations is an important task for IBR. It can be seen that the above measures can improve the super-resolution reconstruction performance of the algorithm, and the three measures have the best effect when used in combination. To learn about super-resolution techniques, just keep reading. • Super-resolution reconstruction deals with the recovery of a single high-resolution image from a set of warped, blurred and decimated versions of it. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. In this paper, we address the super-resolution (SR) problem exploiting a newly introduced adaptive quantum denoiser which is based on quantum interaction theory applied in an imaging context. Specifically, the algorithm is the basis of the Super-Res Zoom feature, as well as the default merge method in Night Sight mode (whether zooming or Oct 5, 2016 · The previous research highlight on image super-resolution. Google's Super Resolution algorithm is one such method. In view of the above problems, an X-ray image super-resolution Sep 22, 2021 · With the development of convolutional neural network, video super-resolution algorithm has achieved remarkable success. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. However, all currently available methods focus on reconstructing texture details, resulting in blurred edges and incomplete structures in the reconstructed images. Duanmu, J. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. The state-of-the-art algorithms are CNN-based that require LR/HR paired training data with contrasts and resolutions that closely match the subject data. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. In 2014, Yang et al. This paper introduces a novel hyperspectral image super-resolution a high resolution (HR) image from one or more observed low resolution (LR) images [16]. The key is to replace the sums of squares by the sum of absolute values (L 1 norm) in the ridge penalty. 1 for C++ and OpenCV version 4. In this work, an image preprocessing algorithm by combining an improved wavelet thresholding function and a generative adversarial network image super-resolution algorithm was proposed. A series of computer modeling experiments, laboratory experiments, and field tests were conducted demonstrating the algorithm’s ability to build high resolution 3D images based on processing multi frames data sets of the same object. . We investigate some excellent algorithms in the field of video space super-resolution based on artificial intelligence, structurally analyze the network structure of the algorithm and May 27, 2021 · Introduction. Firstly, in the To address this problem, a number of super-resolution (SR) algorithms have been developed, including neighbor embedding regressions , random forests (RF) , and convolutional neural networks (CNNs) [5–7]. The proposed method improves the artifact problem and improves the PSNR (peak signal-to-noise ratio) on set5, set10 and bsd100 test sets, and promotes the recovery of high-frequency detail information. Here, we derive a novel hybrid SR network (HSRN Jan 10, 2024 · Super Resolution Optical Imaging and Microscopy Extremely comprehensive resource containing cutting-edge and practical knowledge of super-resolution optical imaging This book covers both the basic principles and specific technical details of super-resolution microscopy techniques. With the rapid developments in deep learning theory and technology, deep learning has been introduced into the field of image super-resolution (SR), and has achieved results far beyond traditional methods The super-resolution algorithm can enhance not only z-plane resolution but also in-plane resolution. In the last two decades, significant progress has been MUSIC Super-Resolution DOA Estimation. J. Blurry images are unfortunately common and are a problem for professionals and hobbyists alike. 3. However, Swin Transformer typically divides the input image into fixed size blocks (such as 48x48) and independently processes each block, causing it to overlook a wider area. Zhu: Image Super-Resolution Algorithm Based on the Dense Space Attention Network FIGURE 6. Due to the limitations of X-ray machines, the acquired images often have some problems, such as blurred details, unclear edges and low contrast, which seriously affect doctors’ interpretations of X-ray images. Several algorithms were already proposed for the solution of this general problem. An improved Orthogonal Matching Pursuit (OMP) algorithm is utilized to estimate the parameter of the simplified attributed scattering center model. Microsc. Aug 1, 2020 · As for image super-resolution IQA, in 2006, Reibman et al. This lesson is the 1st in a 2-part series on Image Super-Resolution: Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques (this tutorial) Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide Higher factors of super-resolution have been obtained by repeated application of this process. The concepts related to super resolution imaging; interpolation and wavelet are covered as background theory. 1: Deconvolution of SRS images using A-PoD. We concentrate on a special case where the warps are pure translations, the blur is space invariant and the same for all the images, and the noise is white. May 29, 2019 · Recent Videos. have proposed an approach combining the classical multi-image super-resolution along with example-based approach and have shown how a super-resolution image can be reconstructed from a single input image. a high resolution (HR) image from one or more observed low resolution (LR) images [16]. Jul 3, 2024 · Title: Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design Authors: Gen Li , Zhihao Shu , Jie Ji , Minghai Qin , Fatemeh Afghah , Wei Niu , Xiaolong Ma View a PDF of the paper titled Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design, by Gen Li and 6 other authors Feb 16, 2023 · Fig. While the dnn_superes module was implemented in C++ back in OpenCV 4. 3+ is pip-installable: $ pip install opencv-contrib-python. 1366 papers with code • 1 benchmarks • 21 datasets. In recent years, over-complete sparse representation has Oct 16, 2023 · super-resolution algorithm (GRTR) based on graph r egular tensor ring decomposition. It involves shooting a burst of raw photos every time the shutter is pressed and takes advantage of the user's natural hand-shake, even if it is ever so slight. Code. Oct 25, 2022 · Single-image super-resolution (SR) has long been a research hotspot in computer vision, playing a crucial role in practical applications such as medical imaging, public security and remote sensing imagery. However, with the advancements in deep learning technologies, deep learning-based super resolutions have gained the Nov 1, 2016 · Super-resolution without explicit motion estimation In recent years, SR methods without explicit motion estimation have become popular. Concerning LR input images, SR techniques can be divided into two main groups, namely single-image super-resolution (SISR) and multiple-image super-resolution or multi-frame super-resolution. The motivation behind these methods is to seek an SR algorithm that is able to process sequences with a general motion pattern. Jun 10, 2021 · This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). To realize super-resolution, spectrum Apr 25, 2024 · The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. 2, the Python bindings were not implemented until OpenCV 4. We propose it for super resolution imaging along with results to put forth that it produces best results qualitatively analyzed using subjective quality measure. In order to solve this problem, we propose a continuous-scale SR algorithm based on a Transformer, which is called Apr 8, 2019 · A multi-emitter fitting algorithm for potential live cell super-resolution imaging over a wide range of molecular densities. As Sep 26, 2018 · This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. Results show that the proposed method outperforms classical interpolation algorithms An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. 1. However, the local quality of the images may The Super Resolution API uses machine learning to clarify, sharpen, and upscale the photo without losing its content and defining characteristics. We propose a super-resolution terahertz NSAR imaging algorithm. Feb 27, 2019 · Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. In this case, even if the number of samples Sep 1, 2021 · Abstract. In contrast to most other deep learning-based super-resolution (SR) methods for MRI [ 13 ], SMORE does not need external training data, which makes it more applicable to a wide variety of acquired MRI pulse Nov 17, 2020 · Table 2 shows the performance values of PSNR and SSIM of this method and the existing general super-resolution algorithm and face super-resolution algorithm TLCRRL. Based on attributed scattering center, a novel algorithm of super-resolution synthetic aperture radar (SAR) imaging is proposed in this paper to characterize physical properties of the scattering object. We investigate some excellent algorithms in the field of video space super-resolution based on artificial intelligence, structurally analyze the network structure of the This study presents a new method for super-resolution of depth images by combining fractional calculus and inverse distance interpolation. Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. Three-dimensional structured illumination microscopy (3D-SIM) is a wide-field super-resolution technique in Nov 6, 2023 · Based on this novel decoding algorithm, a lensless imaging system is proposed, which can handle overlapped and diffracted images with a single random mask. It makes wide use of augmentation. ne ug hu nx sb nv hp if jc ci