k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Shadow Detection Method Based on HMRF with Soft Edges for High-Resolution Remote-Sensing Images

Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.

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.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

The Soft X-ray Imager(SXI)on the SMILE Mission

The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

SMILE soft X-ray Imager flight model CCD370 pre-flight device characterisation

Throughout the SMILE mission the satellite will be bombarded by radiation which gradually damages the focal plane devices and degrades their performance.In order to understand the changes of the CCD370s within the soft X-ray Imager,an initial characterisation of the devices has been carried out to give a baseline performance level.Three CCDs have been characterised,the two flight devices and the flight spa re.This has been carried out at the Open University in a bespo ke cleanroom measure ment facility.The results show that there is a cluster of bright pixels in the flight spa re which increases in size with tempe rature.However at the nominal ope rating tempe rature(-120℃) it is within the procure ment specifications.Overall,the devices meet the specifications when ope rating at -120℃ in 6 × 6 binned frame transfer science mode.The se rial charge transfer inefficiency degrades with temperature in full frame mode.However any charge losses are recovered when binning/frame transfer is implemented.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

Watermarks can provide reliable and secure copyright protection for optical coherence tomography(OCT)fundus images.The effective image segmentation is helpful for promoting OCT image watermarking.However,OCT images have a large amount of low-quality data,which seriously affects the performance of segmentationmethods.Therefore,this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network(RCNN).First,the rough-set-based feature discretization module is designed to preprocess the input data.Second,a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for fusion.Finally,the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in segmentation.RCNN is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data samples.The average dice similarly coefficient(DSC)obtained by RCNN is 6%higher than that of CE-Net.The average 95 percent Hausdorff distance(95HD)and average symmetric surface distance(ASD)obtained by RCNN are 32.4%and 33.3%lower than those of MultiResUNet,respectively.We also evaluate the effect of feature discretization,as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different models.The experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images,providing strong support for its application in medical image watermarking.

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.

CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is proposed.The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature information.At the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image information.This study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional algorithms.The methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other algorithms.The algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)index.Comprehensive experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Design and performance evaluation of a large field-of-view dual-particle time-encoded imager based on a depth-of-interaction detector

Time-encoded imaging is useful for identifying potential special nuclear materials and other radioactive sources at a distance.In this study,a large field-of-view time-encoded imager was developed for gamma-ray and neutron source hotspot imaging based on a depth-of-interaction(DOI)detector.The imager primarily consists of a DOI detector system and a rotary dual-layer cylindrical coded mask.An EJ276 plastic scintillator coupled with two SiPMs was designed as the DOI detector to increase the field of view and improve the imager performance.The difference in signal time at both ends and the log of the signal amplitude ratio were used to calculate the interaction position resolution.The position resolution of the DOI detector was calibrated using a collimated Cs-137 source,and the full width at half maximum of the reconstruction position of the Gaussian fitting curve was approximately 4.4 cm.The DOI detector can be arbitrarily divided into several units to independently reconstruct the source distribution images.The unit length was optimized via Am-Be source-location experiments.A multidetector filtering method is proposed for image denoising.This method can effectively reduce image noise caused by poor DOI detector position resolution.The vertical field of view of the imager was(-55°,55°)when the detector was placed in the center of the coded mask.A DT neutron source at 20 m standoff could be located within 2400 s with an angular resolution of 3.5°.

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

Performance optimization of the neutron-sensitive image intensifier used in neutron imaging

As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution.However,the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied.To optimize the performance of the neutron-sensitive image intensifier at different voltages,experiments have been performed at the China Spallation Neutron Source(CSNS)neutron beamline.The change in the light yield and imaging quality with different voltages has been acquired.It is shown that the image quality benefits from the high gain of the microchannel plate(MCP)and the high accelerating electric field between the MCP and the screen.Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality.Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the n MCP.These results offer a development direction for image intensifiers in the future.

Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.