Multiplicative Speckle Noise Research Articles

MFR-Net: A multi-feature fusion phase unwrapping method for different speckle noises

Phase unwrapping is a crucial step in laser interferometry for obtaining accurate physical measurement of object. To reduce the impact of speckle noise on wrapped phase during actual measurement and improve the subsequent measurement accuracy, a multi-feature fusion phase unwrapping method for different speckle noises named MFR-Net is proposed in this paper. The network is composed of a front-end multi-module filter processing layer and a back-end network with dilated convolution and coordinate attention mechanism. By reducing random phase differences introduced by different levels of noise, the network enhances its capability to extract spatial features such as gradient information between pixels under speckle noise, so that it successfully unwraps the wrapped phase with different speckle noises and accurately recovers the real phase information. Taking the wrapped phases with multiplicative speckle noise and additive random noise as dataset, the results of ablation and comparison experiments show that the MFR-Net has superior unwrapped results. Under three different levels of speckle noise, the average values of MSE, SSIM, PSNR and AU for MFR-Net are at least improved by 84.80 %, 10.99 %, 29.00 % and 7.72 %, respectively, compared to PDVQG, TIE, DLPU and VURNet algorithms. When the standard deviation of speckle noise varies continuously in the range [1.0, 2.0], the average values of four indexes reaches 0.12 rad, 0.91, 31.80 dB and 99.96 %, respectively, indicating the stronger robustness of MFR-Net. In addition, the phase step unwrapping is performed by MFR-Net. Compared to DLPU and VURNet, MFR-Net method reduced MSE by 80 % and 87.35 %, respectively, demonstrating the outstanding generalization capability. The proposed MFR-Net can realize the correct phase unwrapping under different speckle noises. It may be applied in laser interferometry applications such as digital holography and interferometric synthetic aperture radar.

An improved KAZE algorithm for SAR image matching

ABSTRACT The KAZE algorithm has been popularly used to match the remote sensing images. However, it has limited performance when its standard form is directly applied to match the synthetic aperture radar (SAR) images containing significant multiplicative speckle noise. In this paper, an effective SAR image matching algorithm is proposed, which is a combination of KAZE, phase congruency, and speckle noise removing anisotropic diffusion. In this algorithm, the ratio-based Phase Congruency (PC) information is used to eliminate the erroneous features, which improves the repeatability rate of the KAZE features. In addition, the scale layers of the input SAR images are constructed by speckle noise removing anisotropic diffusion method which significantly reduces the effect of noise. The proposed method can improve the repeatability of the extracted KAZE features. Moreover, it can give better recall and precision values than the state-of-the-art methods. It gives the root mean square error (RMSE) values in the range of 0.881 to 1.104 pixels in SAR image matching. Experiments on multi-temporal SAR images demonstrate the applicability of the proposed method.

Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.

Speckle Reduction in Digital Holography by Fast Logistic Adaptive Non-Local Means Filtering

Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a fast logistic adaptive non-local means (LA-NLM) algorithm. In the proposed method, the logistic function is incorporated into the weight calculation of the NLM algorithm to account for multiplicative speckle noise. Filtering parameters are dynamically adjusted according to the statistical property of speckle in the reconstructed image. To enhance computational efficiency, the proposed algorithm takes advantage of the integral image technique to speed up the calculation of the similarity between image patches. Simulated and experimental digital holograms are obtained to verify the proposed method. The results show that the speckle noise is effectively suppressed in digital holography. The proposed method is efficient and feasible, and can be applied to such fields as three-dimensional display, holographic measurement, and medical diagnosis.

Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This non-linear property makes despeckling of imaging data an intractable problem. To enhance despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multi-scale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network (FPN), is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of MSANN, a multi-scale feature enhancement attention module (FEAM) is incorporated into MSANN to extract multi-scale features for improving imaging quality. A multi-scale mixed loss function is further presented to increase network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of MSANN. Experimental results have shown that MSANN has the capacity of effectively removing speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures.

A Gaussian filter-based window for efficient SAR image matching

ABSTRACT Repeatability of the extracted features and the distinctiveness of the descriptors have been the critical factors in synthetic aperture radar (SAR) image matching. These factors are significantly affected by the presence of multiplicative speckle noise in SAR images. In order to address these issues, a Gaussian filter-based SAR image matching scheme is proposed in this paper. At first, the scale-invariant feature transform is combined with the Gaussian filter-based ratio operator for the extraction of more repeatable features from the input images. Then, the descriptors of the extracted features are constructed by using a Gaussian filter-based window. Finally, the input SAR images are matched through a feature-matching operation. The proposed method can obtain better repeatability and more correct matches than the state-of-the-art methods.

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization.

Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.

Two-dimensional phase unwrapping based on U2-Net in complex noise environment.

This paper proposes applying the nested U2-Net to a two-dimensional phase unwrapping (PU). PU has been a classic well-posed problem since conventional PU methods are always limited by the Itoh condition. Numerous studies conducted in recent years have discovered that data-driven deep learning techniques can overcome the Itoh constraint and significantly enhance PU performance. However, most deep learning methods have been tested only on Gaussian white noise in a single environment, ignoring the more widespread scattered noise in real phases. The difference in the unwrapping performance of deep network models with different strategies under the interference of different kinds of noise or drastic phase changes is still unknown. This study compares and tests the unwrapping performance of U-Net, DLPU-Net, VUR-Net, PU-GAN, U2-Net, and U2-Netp under the interference of additive Gaussian white noise and multiplicative speckle noise by simulating the complex noise environment in the real samples. It is discovered that the U2-Net composed of U-like residual blocks performs stronger anti-noise performance and structural stability. Meanwhile, the wrapped phase of different heights in a high-level noise environment was trained and tested, and the network model was qualitatively evaluated from three perspectives: the number of model parameters, the amount of floating-point operations, and the speed of PU. Finally, 421 real-phase images were also tested for comparison, including dynamic candle flames, different arrangements of pits, different shapes of grooves, and different shapes of tables. The PU results of all models are quantitatively evaluated by three evaluation metrics (MSE, PSNR, and SSIM). The experimental results demonstrate that U2-Net and the lightweight U2-Netp proposed in this work have higher accuracy, stronger anti-noise performance, and better generalization ability.

Difference-guided multiscale graph convolution network for unsupervised change detection in PolSAR images

Image change detection is important in polarimetric synthetic aperture radar (PolSAR) image analysis and interpretation. However, improving its accuracy is challenging because of the interference of multiplicative speckle noise. To address this issue, we propose an unsupervised PolSAR image change detection method based on a multiscale graph convolutional network (GCN). First, a Shannon entropy difference image is introduced and improved to obtain an enhanced difference image (EDI) that can effectively suppress speckle noise while preserving edge information. The generated EDI can be further utilised to construct a pseudo-label set required for unsupervised change detection. Subsequently, a difference constraint joint graph construction (DCJGC) module is proposed to obtain the object-level input information of the network. This uses the joint superpixels of multitemporal PolSAR images as graph nodes, and then introduces the difference information in the EDI to constrain the formation process of the edges between the nodes, efficiently and accurately constructing undirected graphs. Finally, a difference-guided multiscale GCN (DGMGCN) is designed for PolSAR image change detection. The network utilises difference information to eliminate the adverse effect of speckle noise on change detection and fully capture the change-aware features of multitemporal PolSAR images at fine and coarse scales, thereby improving feature discriminability. Experimental results on six real Gaofen-3 PolSAR datasets validate the superiority of the proposed approach over other state-of-the-art methods.

A Review on Speckle Reduction Techniques in SAR images

Synthetic Aperture Radar (SAR) is satellite imagery that has multiple applications in variegated fields but is often corrupted by single dependent multiplicative speckle noise. Its multiplicative nature decreases scope for image perception, recognition & limits SAR images applications. Thus, increasing the need for effective & astute SAR image despeckling techniques that not only excise speckle noise but also preserve SAR imageries features, details, and resolution quality. This study analyses various research literature & techniques namely, Adaptive Speckle Reduction Filter, Conditional Averaging filter, Speckle Reduction Filter, Anisotropic diffusion, Speckle Reduction Filter and Speckle Reduction Filter from theoretical, quantitative & qualitative aspects using indexes like SSIM, and RMSE to discover the comparatively superior approach.

A new local structural similarity fusion-based thresholding method for homomorphic ultrasound image despeckling in NSCT domain

Ultrasound is a diagnostic imaging technique to detect various illnesses related to the body's internal organs. It is a non-invasive, safe, cost-effective, and simple imaging modality that uses high-frequency sound waves and their reflection from internal organs. However, a key drawback of using ultrasound imaging tools is that they are often contaminated by multiplicative speckle noise, which can reduce the image resolution and contrast and complicates the diagnosis of the tiny structure of lesions. Extensive studies have shown that eliminating speckle noise is a necessary pre-processing task for computer-assisted ultrasound image systems. This paper proposes a novel method for denoising, weighted thresholding, and fusion of ultrasound images in the Non-Subsampled Contourlet Transformation (NSCT) domain. The method utilizes (a) The Bayesian and Bivariate shrinkage rule in the NSCT domain, (b) A new local structural similarity-fusion-based homomorphic ultrasound image despeckling method in the NSCT domain, and (c) A weights estimation approach based on a bilateral method that helps improve noise suppression and edge preservation results. The performance of the proposed algorithm is evaluated in terms of mean squared error (MSE), peak signal-to-noise ratio, structural similarity index (SSIM), speckle suppression index, signal-to-MSE ratio, and speckle signal-to-noise ratio. Extensive computer simulation results show that the presented method performs better in subjective visual inspection than existing state-of-the-art despeckling techniques.

An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based on the Feature Post-Processing Module

Synthetic Aperture Radar (SAR) can provide rich feature information under all-weather and day-night conditions because it is not affected by climatic conditions. However, multiplicative speckle noise exists in SAR images, which makes it difficult to accurately identify some fuzzy targets in SAR images, such as roads and rivers, during semantic segmentation. This paper proposes an improved Deeplabv3+ network that can be effectively applied to the semantic segmentation task of SAR images. Firstly, this paper added the attention mechanism and, combined with the idea of an image pyramid, proposed the Feature Post-Processing Module (FPPM) to post-process the network output feature map, obtain better fine image features, and solve the problem of fuzzy texture and spectral features of SAR images. Compared to the original Deeplabv3+ network, the segmentation accuracy has been improved by 3.64% and mIoU improved by 1.09%. Secondly, to solve the problems of limited SAR image data and an unbalanced sample, this paper used the focal loss function to improve the backbone function of the network, which increased the mIoU by 1.01%. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module was improved and the 3 × 3 void convolution in ASPP was decomposed into 2D, which can maintain the void ratio and effectively reduce the calculation amount of the module, shorten the training time by 19 ms and improve the semantic segmentation effect.

GradDT: Gradient-Guided Despeckling Transformer for Industrial Imaging Sensors

The speckle noise is a granular disturbance that often brings negative side effects on the detection and recognition of targets of interest in industrial imaging sensors. From the statistical point of view, this type of noise can be modeled as a multiplicative formula. The nonlinear multiplicative property makes despeckling more intractable with respect to noise reduction and details preservation. To blindly remove the undesirable speckle noise, we combine the gradient model and machine learning technology for despeckling. In particular, we first introduce the logarithmic transformation to transform the multiplicative speckle noise into an additive version. A gradient-guided despeckling transformer (termed GradDT) is then proposed to blindly reduce the additive noise in the transformed noisy images. To be specific, the proposed method mainly includes two modules, i.e., the spatial feature extraction module (SFEM) and the efficient transformer module (ETM). The SFEM can extract the spatial feature of speckle noise and the gradient maps corresponding to the noise-free image. The ETM module can calculate the spatial domain's cross-channel cross-covariance and produce global attention maps to reconstruct the sharp image. The proposed GradDT thus can effectively distinguish the speckle noise and vital image features (e.g., edge and texture) to balance the degree of noise suppression and details preservation. Extensive experiments have been implemented on both synthetic and realistic degraded images. Compared with several state-of-the-art speckle noise reduction methods, our GradDT could generate superior imaging performance in terms of both quantitative evaluation and visual quality.

Blind Super-Resolution for SAR Images with Speckle Noise Based on Deep Learning Probabilistic Degradation Model and SAR Priors

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.

Underwater Forward-Looking Sonar Images Target Detection via Speckle Reduction and Scene Prior

Forward-looking sonar (FLS) imagery system plays a significant role in oceanic object recognition and detection since it can overcome the limitation of lighting conditions and reflect the real situation of the underwater environment. However, object detection algorithms for FLS images remain challenging for two main reasons: 1) The noise caused by the coherent characteristic of the scattering phenomenon impairs the detector capture of target information; 2) The scene prior based on the uneven target scale distribution is generally neglected, which leads to the detector generating redundant anchors and slows down detection efficiency. Confronting such challenges, this paper characterizes the noise and the uneven target scale distribution in FLS images as multiplicative speckle noise and scene prior, respectively. Therefore, we propose a novel underwater FLS image detection network, namely UFIDNet, to further improve detection performance by considering speckle noise reduction and scene prior in FLS images. More specifically, a speckle reduction auxiliary branch (SRAB) is designed to introduce additional despeckled supervision information to encourage the feature extractor to produce clean features and share them with the detection pipeline during the training phase. In particular, the noise distribution of FLS images is excavated for synthetic dataset construction and Despeckle Network (DSN) design to obtain despeckled supervision images. In addition, a feature selection strategy (FSS) embedded in detection branch is designed to screen out feature levels that do not match the target size, thus significantly reducing the generation of redundant anchors and improving detection speed. Experimental results show that our UFIDNet achieves 70.5% and 47.3% average precision (AP), 81.3% and 54.6% average recall (AR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max=10</sub> ), 27.0 and 26.1 FPS on two real FLS datasets respectively, outperforming many state-of-the-art general detectors and sonar image detectors.

Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection

Skin cancer is among the most common cancer types worldwide. Automatic identification of skin cancer is complicated because of the poor contrast and apparent resemblance between skin and lesions. The rate of human death can be significantly reduced if melanoma skin cancer could be detected quickly using dermoscopy images. This research uses an anisotropic diffusion filtering method on dermoscopy images to remove multiplicative speckle noise. To do this, the fast-bounding box (FBB) method is applied here to segment the skin cancer region. We also employ 2 feature extractors to represent images. The first one is the Hybrid Feature Extractor (HFE), and second one is the convolutional neural network VGG19-based CNN. The HFE combines 3 feature extraction approaches namely, Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF) into a single fused feature vector. The CNN method is also used to extract additional features from test and training datasets. This 2-feature vector is then fused to design the classification model. The proposed method is then employed on 2 datasets namely, ISIC 2017 and the academic torrents dataset. Our proposed method achieves 99.85%, 91.65%, and 95.70% in terms of accuracy, sensitivity, and specificity, respectively, making it more successful than previously proposed machine learning algorithms.

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.

Improved detection and analysis of Macular Edema using modified guided image filtering with modified level set spatial fuzzy clustering on Optical Coherence Tomography images

Background and Objective: The hypo-reflective Optical Coherence Tomography (OCT) imaging of the pseudo retinal layer strengthens the analysis of various retinal disorders, including Macular Edema (ME)/Diabetic Macular Edema (DME). The primary challenge in the automatic identification and analysis of ME cases is the presence of multiplicative speckle noise and variational edge boundaries. This paper presents an automated algorithm for detecting ME and cyst segmentation. Methods: The algorithm is implemented in three steps. The first step includes the removal of OCT’s predominating speckle noise. In this regard, edge-preserving modified guided image filtering (mGIF) has been employed. After that, the boundaries of retinal layers are segmented using a modified level set spatial fuzzy clustering (LSFCM) algorithm. This process identifies the inner limiting membrane and retinal pigment epithelium layer. The macular thickness between these layers is essential for the detection of edema cases. The third step identifies the presence of cystoid fluid inside positive edema cases using a modified Nick’s threshold, followed by modified LSFCM, applied to mGIF filtered green channel OCT images. Results: The algorithm is tested on a local database (DS1) containing 260 OCT (187 normal and 73 diseased) images. The algorithm’s performance is validated using standard database verification protocols considering the Duke University DME database (DS2) and OPTIMA database (DS3). The databases are annotated by two professional ophthalmologists. The method records an average 97.37% accuracy and an F1-score of 98.69% in detecting ME cases in the DS1 and DS2 databases. The average sensitivity, specificity, accuracy, precision, recall, and F1-score obtained for cyst detection are 98.92%, 99.22%, 98.95%, 98.33%, 98.92% and 98.25%, respectively, obtained from DS1, DS2 database positive ME cases, and DS3 database. The algorithm enhances OCT analysis with comparable efficiency over current state-of-the-art methodologies. Conclusion: The algorithm enhances OCT analysis with efficient speckle noise removal and variational boundary detection of the OCT layer with comparable efficiency over current state-of-the-art methodologies.

A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images

Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.

A NOVEL EAR DETECTION TECHNIQUE IN NOISY ENVIRONMENT USING MEAN STRUCTURE SIMILARITY INDEX MEASURE

In this paper, an efficient automated ear detection technique that uses skin colour and templatebased methods is presented. There are two steps involved in the proposed ear detection technique. The first step in this technique is to remove non-skin pixels from the input image, which is called skin segmentation, and the second step is to detect the ear structure using Structure Similarity Index Measure (SSIM). Furthermore, the proposed technique can also be used in noisy environments to detect different shaped ears. This is made possible by creating an ear template using Principal Component Analysis (PCA) and Mean Structure Similarity Index Measure (MSSIM) for template matching and ear localization. An evaluation of the proposed technique is performed using the UND-E Database, which consists of 464 side face images of 114 subjects. It has achieved 97% accuracy in detecting the ear even when the ear image has additive Gaussian, impulsive salt-pepper and multiplicative speckle noise with variance 1/m, with template size of mxn. In particular, the proposed technique is well suited for detecting the ear image of a person moving as it can also detect the ear in motion blurred images.