Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

Super-resolution fluorescence polarization microscopy

Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological macromolecules.Several recent super resolution techniques have beenapplied to fluorescence polarization microscopy,achieving dipole measurement at nanoscale.In this review,we summarize both difraction limited and super resolution fluorescence polari-zation microscopy techniques,as well as their applications in biological imaging.

Data Matching of Solar Images Super-Resolution Based on Deep Learning

The images captured by different observation station have different resolutions.The Helioseismic and Magnetic Imager(HMI:a part of the NASA Solar Dynamics Observatory SDO)has low-precision but wide coverage.And the Goode Solar Telescope(GST,formerly known as the New Solar Telescope)at Big Bear Solar Observatory(BBSO)solar images has high precision but small coverage.The super-resolution can make the captured images become clearer,so it is wildly used in solar image processing.The traditional super-resolution methods,such as interpolation,often use single image’s feature to improve the image’s quality.The methods based on deep learning-based super-resolution image reconstruction algorithms have better quality,but small-scale features often become ambiguous.To solve this problem,a transitional amplification network structure is proposed.The network can use the two types images relationship to make the images clear.By adding a transition image with almost no difference between the source image and the target image,the transitional amplification training procedure includes three parts:transition image acquisition,transition network training with source images and transition images,and amplification network training with transition images and target images.In addition,the traditional evaluation indicators based on structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)calculate the difference in pixel values and perform poorly in cross-type image reconstruction.The method based on feature matching can effectively evaluate the similarity and clarity of features.The experimental results show that the quality index of the reconstructed image is consistent with the visual effect.

A SUPER-RESOLUTION APPROACH TO SAR PROCESSING

Compared with other methods, the chirp scaling (CS) algorithm is a novel one for compensating the range migration without any interpolation in SAR imaging. However, its resolution ability can't exceed that of Fourier transformation. To realize the super-resolution ability in the azimuth direction a chirp scaling Burg (CSB) algorithm is proposed in this paper, which can still reserve the advantage of avoiding any interpolation in the process of the two-dimensional space-variant correlation in the CS algorithm.

OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO

Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Super-resolution Restoration of Remote-sensing Images

A novel image restoration scheme, which is super-resolution image restoration algorithm Poisson-maximum-afterword-probability based on Markvo constraint (MPMAP) combined with evaluating image detail parameter D, has been proposed. The advantage of super-resolution algorithm MPMAP incorporated with parameter D lies in the fact that super-resolution algorithm MPMAP model is discrete, which is in accordance with remote-sensing imaging model, and the algorithm MPMAP is proved applicable to linear and non-linear imaging models with a unique solution when noise is not severe. According to simulation experiments for practical images, super-resolution algorithm MPMAP can retain image details better than most of traditional restoration methods; at the same time, the proposed parameter D can help to identify real point spread function (PSF) value of degradation process. Processing result of practical remote-sensing images by MPMAP combined with parameter D are given, it illustrates that MPMAP restoration scheme combined PSF estimation has a better restoration result than that of Photoshop processing, based on the same original images. It is proved that the proposed scheme is helpful to offset the lack of resolution of the original remote-sensing images and has its extensive application foreground.

A Fast Super-Resolution Reconstruction from Image Sequence

Based on the mechanism of imagery, a novel method called the delaminating combining template method, used for the problem of super-resolution reconstruction from image sequence, is described in this paper. The combining template method contains two steps： a delaminating strategy and a combining template algorithm. The delaminating strategy divides the original problem into several sub-problems; each of them is only eonnected to one degrading factor. The combining template algorithm is suggested to resolve each sub-problem. In addition, to verify the valid of the method, a new index called oriental entropy is presented. The results from the theoretical analysis and experiments illustrate that this method to be promising and efficient.

Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm

One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution (SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming. In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency (NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature (RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms. © 2014 Chinese Association of Automation.

QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation

The midcourse ballistic closely spaced objects（CSO） create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolution and trajectory estimation is presented.The algorithm combines the focal plane CSO dynamics and radiation models,proposes a novel least square objective function from the space and time information,where CSO radiant intensity is excluded and initial dynamics（position and velocity） are chosen as the model parameters.Subsequently,the quantum-behaved particle swarm optimization（QPSO） is adopted to optimize the objective function to estimate model parameters,and then CSO focal plane trajectories and radiant intensities are computed.Meanwhile,the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories.Finally,the performance（CSO estimation precision of the focal plane coordinates,radiant intensities,and stereo ballistic trajectories,together with the computation load） of the algorithm is tested,and the results show that the algorithm is effective and feasible.

Radial Basis Function Neural Network Based Super- Resolution Restoration for an Undersampled Image

To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

Super-resolution processing of passive millimeter-wave images based on conjugate-gradient algorithm

This paper designs a 3 mm radiometer and validate with experiments based on the principle of passive millimeter wave （PMMW） imaging. The poor spatial resolution, which is limited by antenna size, should be improved by post data processing. A conjugate-gradient （CG） algorithm is adopted to circumvent this drawback. Simulation and real data collected in laboratory environment are given, and the results show that the CG algorithm improves the spatial resolution and convergent rate. Further, it can reduce the ringing effects which are caused by regularizing the image restoration. Thus, the CG algorithm is easily implemented for PMMW imaging.

Image super-resolution reconstruction based on sparse representation and residual compensation

A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.

Super-Resolution for Face Image with an Improved K-NN Search Strategy

Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.

Super-resolution processing of passive millimeter-wave images based on adaptive projected Landweber algorithm

Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.

Edge-Preserving Depth Map Super-Resolution with Intensity Guidance

Depth discontinuity edge affects the visual quality of synthesized images in 3D image warping.However,it suffers from accuracy degradation when up-sampled from low-resolution depth maps,especially at large scaling factors.To preserve the accuracy of depth discontinuity,a novel joint bilateral depth super-resolution with intensity guidance method is proposed.Particularly,the fast local intensity classification is exploited to estimate depth coefficients in joint bilateral up-sampling for depth maps,so as to eliminate depth discontinuity edge misalignment.Additionally,the proposed method is accelerated on graphic processing units(GPUs)to meet the requirement of realtime application.Experiments demonstrate that our method can preserve the accuracy of depth discontinuity edges after super resolution,leveraging the visual quality of synthesized image in 3D image warping.

Super-resolution inpainting

Image or video resources are often received in poor condition, mostly with noise or defects making the resources hard to read. We propose an effective algorithm based on digital image inpainting. The mechanism can be used in restoring images or video frames with very high noise or defect ratio (e.g., 90%). The algorithm is based on the concept of image subdivision and estimation of color variations. Noises inside blocks of different sizes are inpainted with different levels of surrounding information. The results showed that an almost unrecognizable image can be recovered with visually good result. The algorithm can be further extended for processing motion picture with high percentage of noise.