Equivalent Number Of Looks Research Articles

Utilizing the Sentinel-6 Michael Freilich Equivalent Number of Looks for Sea State Applications

Sentinel-6 Michael Freilich (S6-MF) is the first altimeter operating in a continuous high-rate pulse mode, i.e., interleaved mode. This ensures the generation of low-resolution (LR) mode measurements with a pulse repetition frequency (PRF) of ∼9 kHz (variable along the orbit) for the Ku-band as well as the processing of high-resolution (HR) echoes on ground. This operating mode provides an elevated number of highly correlated single looks with respect to the fewer number, weakly correlated echoes of Jason-3 altimeter. A theoretical model is exploited to envisage the correlation properties of S6-MF pulse limited waveform echoes for different sea-state conditions; after that, the model is validated by comparison with the equivalent number of looks (ENL) empirically estimated from real data. The existence of a significant dependence of the statistical properties on the range is verified, and its impact on the precision and on the accuracy in the estimation of the geophysical parameters is assessed in case of the 9 kHz PRF of S6-MF. By applying pulse decimation before the multilook processing, an investigation on new processing techniques is performed, aimed at exploiting the higher ENL in S6-MF low-resolution mode waveforms. It is shown that a bias of less than 0.4 cm is found for SSH and about 1.5 cm for SWH at SWH = 2 m when the decimated waveforms processing is compared with full high-PRF processing.

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9dB, CNR to 6.3dB, SSIM to 0.9, and equivalent number of looks to 120.8dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7dB, respectively. The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.

Reducing noise in polarization-sensitive optical coherence tomography for high-quality local phase retardation imaging

Local phase retardation (LPR) is increasingly recognized as a crucial biomarker for assessing disease progression. However, the presence of speckle noise significantly challenges its accuracy and polarization contrast. To address this challenge, we propose a signal-processing strategy aimed at reducing the impact of noise on LPR measurements. In this approach, the LPR is reconstructed by polar decomposition after averaging multiple Mueller matrices from different overlapping sub-spectra. To optimize measurement accuracy, we systematically combined and traversed different sub-spectral numbers and bandwidths. By examining the quarter-wave plate and glass slide, high-accuracy phase retardation measurements were successfully verified, and the maximum polarization contrast was improved by 23%. Moreover, experimental results from multi-tissue imaging vividly illustrate that the equivalent number of looks (ENL) and polarization contrast were improved by 18% and 19%, respectively. This outcome indicates that our proposed strategy can effectively reduce the noise spikes, enhancing tissue discrimination capabilities.

Inverse synthetic aperture radar image quality assessment based on PSO-BP neural network

ABSTRACT Based on particle swarm optimization-back propagation (PSO-BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method is proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The PSO-BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts.

Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling.

Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view. In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator. The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors. The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.

Two‐step morphology‐based denoising and non‐local means smoothing improves micro‐computed tomography digital rock images

AbstractDigital rock physics is a workflow that relies on imaging techniques to quickly and cost‐effectively estimate the petrophysical properties of small core samples taken from reservoirs. By using digital representations of rock samples as input, physics‐based simulators can estimate properties such as porosity and permeability. The accuracy of these estimates depends on the quality of the digital volumes generated from micro‐computed tomography scans. To enhance the accuracy, denoising is necessary to reduce image noise caused by various experimental factors like electronic noise and bad pixels. This study introduces a novel two‐step denoising pipeline that combines adaptive morphological filtering with non‐local means smoothing, ensuring both noise reduction and preservation of edges. The effectiveness of the proposed pipeline is assessed through qualitative evaluation using optimal segmentation results and quantitative evaluation using a non‐reference metric and equivalent number of looks. Comparing the results of the two‐step approach with traditional non‐local means and morphology‐based filtering using a multi‐resolution structurally varying bitonic filter, the non‐reference metric and equivalent number of looks values are higher, indicating improved denoising performance. Furthermore, the denoised rock volume is subjected to the next step in the digital rock workflow to compute important petrophysical properties like porosity and permeability. The findings indicate that our proposed pipeline significantly improves the accuracy of estimating physical parameters such as porosity and permeability.

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method.

Performance evaluation of DFT based speckle reduction framework for synthetic aperture radar (SAR) images at different frequencies and image regions

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for remote sensing and geoscience applications. However, the coherent processing of radar signals in PolSAR imaging leads to the presence of speckle noise, which can significantly degrade image quality and limit the accuracy of subsequent analyses. To address this issue, speckle reduction frameworks are often applied to PolSAR images to reduce the noise level and enhance image quality. In this paper, the performance of Discrete Fourier Transform (DFT) based speckle reduction framework is evaluated on different bands (L, C, and P band) against various evaluation metrics like CV, SD, SNR, ENL, SSI and SMPI. The proposed framework is evaluated by comparing filtered and unfiltered images across different parameters, such as mean, standard deviation, coefficient of variation, equivalent number of looks (ENL), variance, and signal-to-noise ratio (SNR) on all three bands for the diagonal elements (T11, T22, and T33) of T3 matrix. These metrics provide a comprehensive evaluation of the proposed framework’s ability to (i) smoothen homogeneous regions, (ii) preserve contours, and (iii) retain polarimetric information. The framework’s ability to reduce speckle noise and improve image quality is demonstrated through a significant reduction in standard deviation, coefficient of variation, and improvement in SNR and ENL values. The proposed framework successfully preserved polarimetric information while effectively suppressing speckle noise. These results suggest that the proposed framework could be a valuable tool for improving PolSAR image quality and enhancing subsequent processing of PolSAR data.

An unsupervised ocean surface waves suppression algorithm based on sub-aperture SAR images

ABSTRACT Ocean waves are the richest small-scale texture on the sea surface, from which valuable information can be inversed. In general, synthetic aperture radar (SAR) images of surface waves will inevitably have an impact on some oceanography follow-up applications due to the intricate motion of the waves. However, existing suppression methods based on image geometric properties or deep learning-based have limitations. Owing to the lack of clean images and the uncertainty of the statistical characteristics of seawater, these methods suppress ocean surface waves while harming other target information. To address this issue, we propose an unsupervised wave suppression algorithm based on sub-aperture SAR images named SAR-Noise2Noise (SAR-N2N). First, SAR-N2N exploits the geometric and time-varying relationship of the sub-aperture images to suppress the wave target selectively through sub-aperture image cancellation. Second, through the method of unsupervised learning, only noisy images are used to suppress ocean surface waves, avoiding heavy dependence on the assumption of the wave distribution. Finally, the proposed regularizer is used as an additional loss to form an end-to-end training process. We explain our approach from a theoretical perspective and further validate it through extensive experiments, including synthetic experiments with various ocean wave distributions in simulated SAR images and real-world images. Training on collocations over SAR images, we demonstrate on test data from simulated images that SAR-N2N can improve peak signal-to-noise ratio (PSNR) up to dB compared with the classical approach. Furthermore, it improves the equivalent number of looks (ENL) value of real-world SAR images by a factor of . All results and the methods are novel in terms of the accuracy achieved, combining the classical approach with deep learning techniques. We conclude that SAR-N2N has the potential to make useful contributions to several oceanographic applications as well as to the near real time (NRT) processing of multiparametric sea states.

Integrated Quantitative Evaluation Method of SAR Filters

An excellent quantitative evaluation method of SAR de-speckling filters needs to contain a comprehensive evaluation of both noise smoothing and edge preservation. However, most existing evaluation models only evaluate a single aspect, while a few comprehensive indicators lack robustness. For this reason, a novel integrated quantitative evaluation method of de-speckling filters is proposed. The proposed evaluation method is weighted by two sub-indicators: the coherent equivalent number of looks and edge preservation evaluation. The evaluation indicator of the coherent equivalent number of looks is built to evaluate the noise-smoothing ability of de-speckling filters, whereas the indicator of edge preservation evaluation is built to evaluate the edge-preserving performance of filtered image. Six filters with an excellent performance, five real synthetic aperture radar images with three bands, four polarization modes, four resolutions, and five common evaluation indexes were used in the experiment. The experimental results show that the evaluation results of the proposed evaluation method were consistent with the visual effect and other indicators, and its feasibility was verified.

Non-Local Means De-Speckling Based on Multi-Directional Local Plane Inclination Angle

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.

Mixed multiscale BM4D for three-dimensional optical coherence tomography denoising

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated.

Multitemporal SAR image despeckling based on non-local theory

In this paper, a multitemporal SAR image despeckling based on non-local theory (NLG-MulSAR) algorithm is proposed, which is improved based on the basic framework of the ratio-based multitemporal SAR image denoising (RABASAR). The temporal and spatial information of a multitemporal SAR image is integrated. The super image and the ratio image acquisition part of the RABASAR algorithm are optimized by the NLG filtering algorithm. The NLG algorithm does not need to transform multiplicative noise into additive noise on a synthetic aperture radar (SAR) image and then filter it. The NLG algorithm uses the nonlinear method to eliminate the influence of strong noise points while preserving image edge features. Based on the number of pixels in the non-local image block, the NLG algorithm avoids the generation of fuzzy noise on the filtered image. In this study, we use seven Gaofen three SAR images captured at different times in the Beijing area as experimental data to evaluate the effect of filtering methods in terms of five objective parameters: signal-to-noise ratio, standard deviation, equivalent number of looks, radiative resolution, and speckle noise index. In addition, based on the ratio image, we propose an index, namely the filtering edge coefficient of a multitemporal SAR image, to evaluate the filtering edge retention characteristics of a multitemporal SAR image. The results show that compared with the RABASAR filtering algorithm, the proposed NLG-MulSAR filtering algorithm can better balance the relationship between multiplicative noise and texture detail information and attenuate the speckle while protecting the texture detail information on the SAR image.

On the Method of Circular Polarimetric SAR Calibration Using Distributed Targets

The channel imbalance and cross-talks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) SAR systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, etc. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and cross-talks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross-polarization and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the cross-talks. Then the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

Improved Weighted Non-Local Mean Filtering Algorithm for Laser Image Speckle Suppression.

Laser speckle noise caused by coherence between lasers greatly influences the produced image. In order to suppress the effect of laser speckles on images, in this paper we set up a combination of a laser-structured light module and an infrared camera to acquire laser images, and propose an improved weighted non-local mean (IW-NLM) filtering method that adopts an SSI-based adaptive h-solving method to select the optimal h in the weight function. The analysis shows that the algorithm not only denoises the laser image but also smooths pixel jumps in the image, while preserving the image details. The experimental results show that compared with the original laser image, the equivalent number of looks (ENL) index of the IW-NLM filtered image improved by 0.80%. The speckle suppression index (SSI) of local images dropped from 4.69 to 2.55%. Compared with non-local mean filtering algorithms, the algorithm proposed in this paper is an improvement and provides more accurate data support for subsequent image processing analysis.

An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF

The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance.

A New Technique Based on AMID Using Adaptive Thresholding for Ultrasound Speckle Reduction

A new technique based on adaptive multi-resolution image decomposition (AMID) using adaptive thresholding for ultrasound speckle reduction has been proposed in this paper. The noisy image is first filtered to reduce large multiplicative noise. The image is then log-transformed before decomposing it to its approximation and detailed coefficients using AMID algorithm. The approximation coefficients are appropriately filtered while adaptive thresholding is applied to the detail coefficients. The image is reconstructed back from all the modified coefficients to obtain the denoised image. This method combining adaptive decomposition with adaptive thresholding makes it adaptive to both signal and noise components, thereby retaining the edge details in the denoised image effectively. The proposed technique performance is tested on synthetic as well as real ultrasound images with the noise of different variances. The experimental results show that the proposed algorithm gives better performance than the other state-of-the-art methods in terms of edge keeping index (EKI), correlation coefficient (CC), figure of merit (FOM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and signal-to-noise ratio (SNR) for synthetic images. The algorithm gives better performance in terms of equivalent number of looks (ENL) and mean-to-variance ratio (MVR) for real ultrasound images.

A generative adversarial network with multi-scale convolution and dilated convolution res-network for OCT retinal image despeckling

Optical coherence tomography (OCT) has been widely adopted for imaging in various areas, yet it is largely affected by speckle noise generated from the coherent multiple-scattered photons. To alleviate the influences of speckle noise, a generative adversarial network with multi-scale convolution and dilated convolution res-network (MDR-GAN) is proposed in this study. Specifically, a cascade multi-scale module (CMSM) consisting of three convolution and dilated convolution res-network (CD-Rn) blocks is proposed to raise network learning capacity, while a new residual learning method is devised to link the input and output feature maps for feature reconstructions. Among them, CMSM has the characteristics of capturing multi-scale local features of images. Residual learning effectively avoids the degradation problem of the network. Extensive experiments with four retinal OCT datasets are conducted and results are compared with those of the state-of-the-art deep learning networks to verify the effectiveness of the proposed MDR-GAN. Results demonstrate that the denoising effect of MDR-GAN is better than those of the other denoising methods. The peak single-to-noise ratio (PSNR) of MDR-GAN is improved by 2 dB as compared that of Pix2pix, while its equivalent number of looks (ENL) is improved by at least 233.9% as compared with the-state-of-the-art existing methods. Our MDR-GAN code can be download at https://github.com/Austin-Lms/MDR-GAN.

SAR‐IRGF: A novel improved rolling guidance filter for the synthetic aperture radar image

Inspired by the framework of the rolling guidance filter (RGF), a novel improved RGF for synthetic aperture radar (SAR) images is proposed in this letter, named SAR-IRGF. Based on the model of additive noise, the RGF has the complete control of detailed smoothing under a scale measure for optical images. However, the speckle is well known as a multiplicative noise, which invalidates the RGF for SAR images. In this letter, an appropriate edge guidance is utilized to optimize the RGF for SAR image denoising. Compared with the original version, better convergence is achieved. Experimental results on natural Ku-band airborne SAR images show that the proposed SAR-IRGF reduces more speckle while sharpening the edges effectively. Simultaneously, the SAR-IRGF demonstrates a significant nearly 5.5-fold improvement than RGF in the equivalent number of looks (ENL).