High-resolution Imaging Research Articles

Unsupervised Test-Time Adaptation Learning for Effective Hyperspectral Image Super-Resolution With Unknown Degeneration.

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multi-spectral image has provided an effective way for HSI super-resolution (SR). The key lies on inferring the posteriori of the latent (i.e., HR) HSI using an appropriate image prior and the likelihood determined by the degeneration between the latent HSI and the observed images. However, in scenarios with complex imaging environments and various imaging scenes, the prior of HSIs can be prohibitively complicated and the degeneration is often unknown, which causes it difficult to accurately infer the posteriori of each latent HSI. To tackle this problem, we present an unsupervised test-time adaptation learning (UTAL) framework for HSI SR under unknown degeneration. Instead of directly modeling the complicated image prior, it first implicitly learns a content-agnostic prior shared across different images through supervisedly pre-training a mutual-guiding fusion module on extensive synthetic data. Then, it adapts the shared prior to those private characteristics in the latent HSI for posteriori inference through unsupervisedly learning a self-guiding adaptation module and a degeneration estimation network on two observed images in the test phase. Such a two-stage learning scheme models the complicated image prior in a divide-and-conquer manner, which eases the modeling difficulty and improves the prior accuracy. Moreover, the unknown degeneration can be estimated properly. Both of these two advantages empower us to accurately infer the posteriori of the latent HSI, thereby increasing the generalization performance in real applications. Additionally, in order to further mitigate the over-fitting in coping with more challenging cases (e.g., degenerations in both spectral and spatial domains are unknown) and speed up, we propose to meta-train UTAL on extensive synthetic SR tasks and solve it using an alternative optimization strategy such that UTAL learns to produce good generalization performance in real challenging cases with a small number of gradient descent steps. To verify the efficacy of UTAL, we evaluate it on HSI SR tasks with different unknown degenerations as well as some other HSI restoration tasks (e.g., compressive sensing), and report strong results superior to that of existing competitors.

High-Resolution Imaging Study on Photodissociation of OCS+ [A2ΠΩ=1/2,3/2 (ν1 0 ν3)

The development of the velocity map ion imaging (VMI) technique has greatly advanced the study of photodissociation dynamics. The high-resolution imaging study of the photodissociation allows for the acquisition of precise and detailed information on the fragments. This information can further provide more insight into the energy partition and potential pathways involved in the photodissociation process. In this study, we report the investigation on the photodissociation of OCS+ via the A2ΠΩ=1/2,3/2 states following the excitation of A2Π (ν1 0 ν3) ← X2Π (0 0 0) by using time-sliced VMI techniques in the ultraviolet region. Our investigation revealed significant mode-dependent recoil anisotropies and branching ratios of two product channels for both Ω = 1/2 and Ω = 3/2. The photolysis products also exhibited dramatic deviation in angular distributions and generally comparable kinetic energy distributions following the excitation to the same vibrational modes of A2ΠΩ states with two separate spin-orbit components. According to the observation in this study and previously reported photodissociation mechanisms of the OCS+ cations, the decay from the A2Π3/2 state was more likely via the internal conversion to high rovibrational states of the X2Π state, in comparison to the A2Π1/2 state.

Assessing the contribution of RGB VIs in improving building extraction from RGB-UAV images

Buildings are a fundamental component of the built environment, and accurate information regarding their size, location, and distribution is vital for various purposes. The ever-increasing capabilities of unmanned aerial vehicles (UAVs) have sparked an interest in exploring various techniques to delineate buildings from the very high-resolution images obtained from UAV photogrammetry. However, the limited spectral information in UAV images, particularly the number of bands, can hinder the differentiation between various materials and objects. This setback can affect the ability to distinguish between different materials and objects. To address this limitation, vegetative ındices (VIs) have been employed to enhance the spectral strength of UAV orthophotos, thereby improving building classification. The objective of this study is to evaluate the contribution of four specific VIs: the green leaf index (GLI), red-green-blue vegetation index (RGBVI), visual atmospherically resistant index (VARI), and triangular greenness index (TGI). The significance of this contribution lies in assessing the potential of each VI to enhance building classification. The approach utilized the geographic object-based image analysis (GeoBIA) approach and a random forest classifier. To achieve this aim, five datasets were created, with each dataset comprising the RGB-UAV image and a corresponding RGB VI. The experimental results on the test dataset and a post-classification assessment indicated a general improvement in the classification when the VIs were added to the RGB orthophoto.

Bright ESIPT emission from 2,6-di(thiazol/oxazol/imidazol-2-yl)phenol derivatives in solution, aggregation and solid states

Most luminophores often suffer from the problem of aggregation-caused quenching (ACQ) or fluorescence disappearance in dilute solution. It is significant to bridge the gap between ACQ and AIE. In this work, a facile but effective strategy was proposed for the fabrication of always-on luminophores based on the excited state intramolecular proton transfer (ESIPT) mechanism, and six luminophores emitting bright fluorescence in solution, aggregation and solid states were synthesized from 5-tert-butyl-2-hydroxyisophthalaldehyde. All these ESIPT systems show only keto emission owing to their congested structures which block the breakage of intramolecular hydrogen bond (O–H⋯N) by solvation, and subsequently make enol emission impossible. Three of these luminophores are prone to convert into the corresponding phenolate anions emitting blue-shifted emission, which enable them to sense pH variation in the weakly basic range. Furthermore, white-light emission was achieved by combining two of them which show complementary-color fluorescence, and one of them was utilized for bioimaging of living Hela cells and the high-resolution image was obtained.

High Fidelity Makeup via 2D and 3D Identity Preservation Net

In this article, we address the challenging makeup transfer task, aiming to transfer makeup from a reference image to a source image while preserving facial geometry and background consistency. Existing deep neural network-based methods have shown promising results in aligning facial parts and transferring makeup textures. However, they often neglect the facial geometry of the source image, leading to two adverse effects: (1) alterations in geometrically relevant facial features, causing face flattening and loss of personality, and (2) difficulties in maintaining background consistency, as networks cannot clearly determine the face-background boundary. To jointly tackle these issues, we propose the High Fidelity Makeup via two-dimensional (2D) and 3D Identity Preservation Network (IP23-Net), to the best of our knowledge, a novel framework that leverages facial geometry information to generate more realistic results. Our method comprises a 3D Shape Identity Encoder, which extracts identity and 3D shape features. We incorporate a 3D face reconstruction model to ensure the three-dimensional effect of face makeup, thereby preserving the characters’ depth and natural appearance. To preserve background consistency, our Background Correction Decoder automatically predicts an adaptive mask for the source image, distinguishing the foreground and background. In addition to popular benchmarks, we introduce a new large-scale High Resolution Synthetic Makeup Dataset containing 335,230 diverse high-resolution face images to evaluate our method’s generalization ability. Experiments demonstrate that IP23-Net achieves high-fidelity makeup transfer while effectively preserving background consistency. The code will be made publicly available.

Detail-preserving Joint Image Upsampling

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this article, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.

A voxel-based magnetic resonance study of cortical gray matter structures in intermittent exotropia

Objective: To explore the changes in gray matter volume of the cerebral cortex in patients with intermittent exotropia (IXT) using the voxel-based analysis and to analyze the correlation between these changes and clinical manifestations. Methods: This was a cross-sectional study. A collection of 15 consecutive patients diagnosed with IXT at Tianjin Eye Hospital from March 2021 to May 2022 formed the exotropia group, which comprised 8 males and 7 females, with an average age of (23.5±5.2) years. Ten healthy individuals, 3 males and 7 females, with an average age of (27.0±7.5) years, were selected as the control group. All participants underwent assessments of exotropia severity and Titmus stereoacuity. Three-dimensional high-resolution brain images were obtained through MRI scans. Voxel-based morphometry was employed to preprocess the MRI data, and the SPM toolbox in MATLAB was utilized to analyze differences of images between the two groups. Regions of interest (ROI) with structural abnormalities in the gray matter volume analysis were selected, and the ratio of gray matter voxel values in the ROI to the mean gray matter voxel values of the whole brain for each participant was calculated using the MarsBaR software. The correlation between this ratio and exotropia severity as well as the common logarithm of Titmus stereoacuity was analyzed. Results: The differences in age, gender distribution, and refractive error between the two groups were not statistically significant (all P>0.05). However, there were statistically significant differences in the degree of strabismus and Titmus stereoacuity (both P<0.001). Compared to the control group, patients in the strabismus group exhibited decreased gray matter volume in several brain regions, including the wedges of the medial surface of the cerebral hemisphere (decreased by 89 voxels), the left lingual gyrus (decreased by 176 voxels), the left calcarine sulcus V3 area (decreased by 30 voxels), the central anterior gyrus of the right frontal lobe (decreased by 192 voxels), the gray matter of the left hippocampal gyrus (decreased by 20 voxels), and the bilateral lateral geniculate nuclei (decreased by 100 and 40 voxels on the left and right sides, respectively). These differences were all statistically significant (all P<0.001). Additionally, there was an increase in gray matter volume in several brain regions, including the bilateral caudate nuclei (increased by 60 and 76 voxels on the left and right sides, respectively) and the left precentral gyrus (increased by 36 voxels). These differences were also statistically significant (all P<0.001). A group-level analysis identified 10 brain regions with structural differences between the two groups, which were used as ROI. The gray matter volume ratio was negatively correlated with the degree of exotropia (all P<0.05) in the ROI of the left wedges (r=-0.670), left calcarine sulcus V3 area (r=-0.610), and left lingual gyrus (r=-0.684). The gray matter volume ratio was negatively correlated with lgTS (all P<0.05) in the ROI of the left wedges (r=-0.568) and the central anterior gyrus of the right frontal lobe (r=-0.563). Conclusions: Patients with IXT exhibit decreased gray matter volume in the horizontal connection areas between the primary visual cortices V1 and V2. The reduction in gray matter volume of the lingual gyrus and the dorsal visual pathway V3 area becomes more pronounced with increasing exotropia severity, while the gray matter volume of the precentral gyrus (BA6 area) decreases with worsening stereoacuity.

Correction of multiplexing artefacts in multi-pinhole SPECT through temporal shuttering, de-multiplexing of projections, and alternating reconstruction

Objective. Single-photon emission computed tomography (SPECT) with pinhole collimators can provide high-resolution imaging, but is often limited by low sensitivity. Acquiring projections simultaneously through multiple pinholes affords both high resolution and high sensitivity. However, the overlap of projections from different pinholes on detectors, known as multiplexing, has been shown to cause artefacts which degrade reconstructed images. Approach. Multiplexed projection sets were considered here using an analytic simulation model of AdaptiSPECT-C—a brain-dedicated multi-pinhole SPECT system. AdaptiSPECT-C has fully adaptable aperture shutters, so can acquire projections with a combination of multiplexed and non-multiplexed frames using temporal shuttering. Two strategies for reducing multiplex artefacts were considered: an algorithm to de-multiplex projections, and an alternating reconstruction strategy for projections acquired with a combination of multiplexed and non-multiplexed frames. Geometric and anthropomorphic digital phantoms were used to assess a number of metrics. Main results. Both de-multiplexing strategies showed a significant reduction in image artefacts and improved fidelity, image uniformity, contrast recovery and activity recovery (AR). In all cases, the two de-multiplexing strategies resulted in superior metrics to those from images acquired with only mux-free frames. The de-multiplexing algorithm provided reduced image noise and superior uniformity, whereas the alternating strategy improved contrast and AR. Significance. The use of these de-multiplexing algorithms means that multi-pinhole SPECT systems can acquire projections with more multiplexing without degradation of images.

Ground-based image deconvolution with Swin Transformer UNet

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.

SCExAO/CHARIS Multiwavelength High-contrast Imaging of the BD+45°598 Debris Disk

We present a multiwavelength (1.16–2.37 μm) view of the debris disk around BD+45°598, using the Subaru Coronagraphic Extreme Adaptive Optics system paired with the Coronagraphic High Angular Resolution Imaging Spectrograph. With an assumed age of 23 Myr, this source allows us to study the early evolution of debris disks and search for forming planets. We fit a scattered light model to our disk using a differential evolution algorithm, and constrain its geometry. We find the disk to have a peak density radius of R 0 = 109.6 au, an inclination of i = 88.1°, and position angle PA = 111.0°. While we do not detect a substellar companion in the disk, our calculated contrast limits indicate sensitivity to planets as small as ∼10M Jup at a projected separation of 12 au of the star, and as small as ∼4M Jup beyond 38 au. When measuring intensity as a function of wavelength, the disk color constrains the minimum dust grain size within a range of ∼0.13–1.01 μm.

A dataset describing chip parameters for rock breaking by chisel pick under deep-sea hydrostatic pressure

Chip is a visual representation of rock breaking by cutter, and their related parameters are crucial for revealing the rock breaking mechanism in deep-sea mining. Based on sieving and three-dimensional size measurement methods widely used in mining engineering, this paper reports a dataset of chip parameters for rock breaking by chisel pick under deep-sea hydrostatic pressure. Specifically, we first designed an experimental setup that can accurately simulate deep-sea hydrostatic pressure, conducted rock breaking experiments and carefully collected chips. Subsequently, those chips were sieved, high-resolution images were collected, and the coarseness index (CI), chip size uniformity (n), absolute chip size (de), and fractal dimension (D) were measured. Finally, three-dimensional size (long, intermediate and short) was measured for 3064 chips with particle sizes greater than 4.75 mm. This dataset will be used by researchers to validate numerical simulations or optimize equipment structures related to deep-sea mining, including deep-sea rock mechanics, mining cutter and conveyor pipes.

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation.

Highly Efficient Fluorescent Probe to Visualize Protein Interactions at the Superresolution.

Superresolution microscopy (SR microscopy) of protein-protein interactions (PPIs) occurring in subcellular structures is essential for understanding cellular functions. However, a powerful and useful technology for SR microscopy of PPIs remains elusive. Here, we develop a highly efficient photoconvertible fluorescent probe, named split-Dendra2, for SR microscopy of PPIs in the cell. We found that split-Dendra2 enables a highly efficient detection of PPIs, making it possible to perform SR microscopy of PPIs with high spatial resolution and high image reconstruction fidelity. We demonstrate the utility of split-Dendra2 by visualizing PPIs occurring in small subcellular structures at the superresolution, such as clathrin-coated pits and focal adhesions, which cannot be visualized by the existing tools. Split-Dendra2 offers a powerful and useful tool that greatly expands the possibility of SR microscopy and can contribute to revealing the function of PPIs at the nanoscale resolution.

Uncertainty quantification for velocity measurement with 2D2C PIV

Abstract In this paper, A theoretical model of dimensionless instantaneous and average velocity measurement uncertainty quantification with 2D2C PIV is established under the framework of generally adopted international uncertainty quantification standards. The effectiveness of the model is verified using uniform flow field testing data. Combined with the semi-quantitative analysis of the theoretical model, uncertainty control suggestions of PIV measurement are given. The major source affecting the uncertainty of instantaneous velocity measurement is reference velocity, particle instantaneous pixel displacement, and their correlation term. For average velocity measurement uncertainty quantification, the uncertainty of particle average pixel displacement is effectively controlled by taking a large number of particle images. Thus three single-component terms: calibration factor, particle average pixel displacement, reference velocity and two correlation terms: particle average pixel displacement-calibration factor, particle average pixel displacement-reference velocity all made an important contribution to average velocity measurement uncertainty. To reduce uncertainty of PIV velocity measurement, one can reduce the reference velocity measurement uncertainty, optimize PIV algorithm and improve the calibration factor by applying high spatial resolution imaging system in experiment. And reducing reference velocity measurement uncertainty and improve space resolution are the key and feasible way.

Observation of Io's Resurfacing via Plume Deposition Using Ground‐Based Adaptive Optics at Visible Wavelengths With LBT SHARK‐VIS

AbstractSince volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground‐based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground‐based telescope. These images, acquired by the SHARK‐VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io's trailing hemisphere. When compared to the most recent spacecraft images, the SHARK‐VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long‐lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth‐based telescopes. The SHARK‐VIS instrument ushers in a new era of high resolution imaging of Io's surface using adaptive optics at visible wavelengths.

High Performance Mg Alloy with Designed Microstructure and Phases.

A high strength and ductile Mg-Gd-Y-Zn-Zr alloy was designed and fabricated. The local strain evolution of the alloys during plastic deformation was analyzed using high-resolution digital image correlation (DIC). The results showed that the β particles, nano-sized γ' phases, and LPSO phases were distributed in the as-extruded alloy and a bimodal microstructure was exhibited, including elongated un-dynamic recrystallized grains and fine dynamic recrystallized grains. With increasing extrusion ratio, the grain size remained, with the volume fraction of dynamic recrystallization of the as-extruded alloy increasing from 30% to 75%, and the as-extruded alloy exhibited a high strength-ductility synergy, which is attributed to the grain refinement, extensive β particles, and elongated block-shaped LPSO phases. The strain evolution analysis showed that a strain-transfer from un-DRXed regions to adjacent DRXed regions and LPSO phases can promote uniform plastic deformation, which tends to improve the ductility of the alloy.

Asymmetric magma plumbing system beneath Axial Seamount based on full waveform inversion of seismic data

The architecture of magma plumbing systems plays a fundamental role in volcano eruption and evolution. However, the precise configuration of crustal magma reservoirs and conduits responsible for supplying eruptions are difficult to explore across most active volcanic systems. Consequently, our understanding of their correlation with eruption dynamics is limited. Axial Seamount is an active submarine volcano located along the Juan de Fuca Ridge, with known eruptions in 1998, 2011, and 2015. Here we present high-resolution images of P-wave velocity, attenuation, and estimates of temperature and partial melt beneath the summit of Axial Seamount, derived from multi-parameter full waveform inversion of a 2D multi-channel seismic line. Multiple magma reservoirs, including a newly discovered western magma reservoir, are identified in the upper crust, with the maximum melt fraction of ~15–32% in the upper main magma reservoir (MMR) and lower fractions of 10% to 26% in other satellite reservoirs. In addition, a feeding conduit below the MMR with a melt fraction of ~4–11% and a low-velocity throat beneath the eastern caldera wall connecting the MMR roof with eruptive fissures are imaged. These findings delineate an asymmetric shallow plumbing system beneath Axial Seamount, providing insights into the magma pathways that fed recent eruptions.

Open the Window to Lithium Detection and Nanostructure Analysis

Abstract The features and benefits of a new cutting-edge windowless energy-dispersive detector (EDS) detector are introduced. This innovative technology provides fast results at high signal-to-noise with ultra-high analytical spatial resolution. The benefits are demonstrated through examples of nanostructures from various nanoscale devices including lithium metal anode specimens. Using windowless EDS detector technology allows for the efficient collection of EDS data under the same conditions used for high-resolution imaging (low accelerating voltage, low probe current, and short working distance). Examples include cross sections of lithium metal anode materials prepared with a broad argon ion beam and using an air-isolated workflow. The analyses of the cycled and pristine lithium-ion battery (LIB) materials are shown, along with the contrast results of crystal-shaped impurities found within these materials.

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance. In this paper, we propose a cross-modality reference and feature mutual-projection (CRFM) method to enhance the spatial resolution of brain MRI images. Specifically, we feed the gradients of HR MRI images from referenced imaging modality into the SR network to transform true clear textures to LR feature maps. Meanwhile, we design a plug-in feature mutual-projection (FMP) method to capture the cross-scale dependency and cross-modality similarity details of MRI images. Finally, we fuse all feature maps with parallel attentions to produce and refine the HR features adaptively. Extensive experiments on MRI images in the image domain and k-space show that our CRFM method outperforms existing state-of-the-art MRI SR methods.

Meta-learning based blind image super-resolution approach to different degradations

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.