12,898 Hits in 6.1 sec

Hierarchic Voronoi skeletons

R.L. Ogniewicz, O. Kübler
1995 Pattern Recognition  
Each component of the VD is attributed with a measure of prominence which exhibits the expected invariance under geometric transformations and noise.  ...  Robust and time-efficient skeletonization of a (planar) shape, which is connectivity preserving and based on Euclidean metrics, can be achieved by first regularizing the Voronoi diagram (VD) of a shape's  ...  Pruning by removing all edges with ∆5 6 ¡ ¢ ¦ ¥ ( ¥ : threshold value) thus preserves the topology of the skeleton.  ... 
doi:10.1016/0031-3203(94)00105-u fatcat:zph4ac473bbgpjjoxu36wjxc6a

A Novel Image Denoising and Segmentation Using Machine Learning with SRM Strategy

Swaroopa H Narayan, Department of PG Studies and Research in Electronics, Kuvempu University, Shankaraghatta, Shimoga, 577451, Karnataka, India, Basavaraj N Jagadale, Omar Abdullah Murshed Farhan Alnaggar
2022 Indian Journal of Science and Technology  
involves 29 essential features with retained residual details at level-2 decomposition and is compared with leading segmentation techniques.  ...  The Discrete Wavelet Transform (DWT) is primarily used as a pre-processing tool, while edge details of the decomposed image are automatically preserved with the help of machine understanding.  ...  However, detailed coefficients contain edge/texture details of the image along with residual noise.  ... 
doi:10.17485/ijst/v15i17.2203 fatcat:kxaeegivbjg7bemdvt5zp4r4sm

Wavelet analysis of instrumented rocket motor data

D. H. Smith
2004 ANZIAM Journal  
Second generation wavelets by the lifting scheme are applied to irregularly sampled data from instrumented rocket motors and compared with a complementary discrete Fourier approach.  ...  Noise reduction by coefficient thresholding Thresholding is the selective removal or reduction of wavelet coefficients with the aim of producing a "cleaned" signal upon inversion.  ...  which can be viewed in several ways.  ... 
doi:10.21914/anziamj.v45i0.876 fatcat:kvhc742dtff7xgc2ynskc5nfoa

The Bitonic Filter: Linear Filtering in an Edge-Preserving Morphological Framework

Graham Treece
2016 IEEE Transactions on Image Processing  
However, it gives good visual results in all circumstances, with characteristics which make it appropriate particularly for signals or images with varying noise, or features at varying levels.  ...  Index Terms-Gaussian filter, median filter, morphology, noise reduction, edge preservation. 1057-7149  ...  leave residual noise close to edges.  ... 
doi:10.1109/tip.2016.2605302 pmid:27608460 fatcat:3sath4ga5nc7ffkv6i5n5xfl5q

A wavelet multiscale denoising algorithm for magnetic resonance (MR) images

Xiaofeng Yang, Baowei Fei
2011 Measurement science and technology  
Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities.  ...  Based on the nature of Rician noise we estimate noise variance in different scales.  ...  Our method can not only effectively decrease Rician noise in MRI but also preserve the key image details and features.  ... 
doi:10.1088/0957-0233/22/2/025803 pmid:23853425 pmcid:PMC3707516 fatcat:b2gfapizvvhbteknrcf43rcwua

Solving a variational image restoration model which involvesL constraints

Stéphane Lintner, François Malgouyres
2004 Inverse Problems  
The resulting nonlinear programming task is solved via a dual Uzawa method in its general form, leading to an efficient and general algorithm which allows for very good structure-preserving reconstructions  ...  Along with a theoretical study of the algorithm, the paper details some aspects of the implementation, discusses the numerical convergence and eventually displays a few images obtained for some difficult  ...  Note by the way that the constraints can be rewritten as | u, H * ( i ) − v, i | τ , which shows that D should be designed so as to carry information along the thresholding features H * ( ).  ... 
doi:10.1088/0266-5611/20/3/010 fatcat:guyy7dj2ezfqhfq2lwq7hh4fea

Performance Analysis Survey of Various SAR Image Despeckling Techniques

A. Rajamani, V. Krishnaveni
2014 International Journal of Computer Applications  
However, these images are affected with granular noise termed as Speckle noise.  ...  This paper presents the various important techniques available till date for the removal of speckle noise from SAR images and each technique with its own advantages and limitations are described.  ...  It is advantageous than all other spatial domain filters by the best way of preserving important edges, linear features and fine details.  ... 
doi:10.5120/15584-4254 fatcat:ld7hmft4cbeqje2conp3eroojm

Theoretical aspects of vertically invariant gray-level morphological operators and their application on adaptive signal and image filtering

Chu-Song Chen, Ja-Ling Wu, Yi-Ping Hung
1999 IEEE Transactions on Signal Processing  
Experimental results have shown that our approach can successfully eliminate noises without oversmoothing the important features of a signal.  ...  Vertically invariant openings and closings have intuitive geometric interpretations and can provide different filtering scales with respect to different spacial positions.  ...  remove noises while preserving features by varying the filtering scales with respect to spatial positions [1] , [20] , [24] , [27] , [31] .  ... 
doi:10.1109/78.752602 fatcat:linpsamm2bguvd75wtgoqvvwf4

Cancellation of polarized impulsive noise using an azimuth-dependent conditional mean estimator

U. Spagnolini
1998 IEEE Transactions on Signal Processing  
Noise is a superposition of M non-Gaussian processes, each with a fixed azimuth of polarization. Neither the number of processes (M ) nor their azimuths are known.  ...  Circular symmetry is suitable for the nonpolarized noise vector, whereas the azimuth-dependent estimator is tailored to polarized noise.  ...  sectors with the coupling through the background model Because of the way the A-ZNL estimator has been defined, the azimuth is preserved as for the isotropic noise model.  ... 
doi:10.1109/78.735308 fatcat:7mxwiwemvzcp7j4ntzhpeaxcoy

BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring [article]

Pengwei Liang, Junjun Jiang, Xianming Liu, Jiayi Ma
2021 arXiv   pre-print
In this way, we can expect to well maintain the information of the clear regions while recovering the missing details of the blurred regions.  ...  Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities (i.e. parameters) to process  ...  Just as one expected, when the lighter branch with less output residual features takes part in deblurring, the network pays attention to preserve the details in the in-focus region.  ... 
arXiv:2105.14766v1 fatcat:ll44nsmjyzbndh42j76drafjsy

Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

Rabia Naseem, Faouzi Alaya Cheikh, Azeddine Beghdadi, Khan Muhammad, Muhammad Sajjad
2021 Electronics  
The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image.  ...  PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image.  ...  Both MCDN [24] and FFD-Net [53] effectively remove the noise. Similarly, CMGDNet also eradicates noise with reasonable preservation of the structural information.  ... 
doi:10.3390/electronics10222855 fatcat:qxivy4pezvdablfxdsefabqduu

Proximal approach to denoising hyperspectral images under mixed-noise model

Hazique Aetesam, Kumari Poonam, Suman Kumar Maji
2020 IET Image Processing  
Since TV possesses feature selection capability by setting some of the coefficients to zero, it works well when there are a small number of significant features.  ...  A combination of Gaussian-impulse noise has been handled under maximum a posteriori framework using two data fidelity terms.  ...  A multi-level feature representation and extraction for learning spatio-temporal features using a deep residual convolutional network is used in [26] .  ... 
doi:10.1049/iet-ipr.2019.1763 fatcat:fm3wttchqrgj3fulxkmho7xobe

A Novel Gray Image Denoising Method Using Convolutional Neural Network

Yizhen Meng, Jun Zhang
2022 IEEE Access  
In this way, the information of the neurons with negative thresholds is preserved and the fitting ability of the network is also improved.  ...  Con-vNet, designing symmetric fading feature extraction, dual activation functions and dual residual blocks, and combining with a batch normalization algorithm, an end-to-end trainable denoising ConvNet  ... 
doi:10.1109/access.2022.3169131 fatcat:uom37pgrk5hebmasts4jamwj2q

Dual-Frequency Airborne SAR for Large Scale Mapping of Tidal Flats

Muriel Pinheiro, Joel Amao-Oliva, Rolf Scheiber, Marc Jaeger, Ralf Horn, Martin Keller, Jens Fischer, Andreas Reigber
2020 Remote Sensing  
, e.g., small terrain fissures, can be spread throughout the lower scales along noise.  ...  Moreover, dual-frequency filtering strategies will be employed to further reduce the noise level (see Section 4.2.7).  ... 
doi:10.3390/rs12111827 fatcat:pi3qbd4qvfggdinfi7cwmlx75u

Optical Predistortion Enabling Phase Preservation in Optical Signal Processing Demonstrated in FWM-Based Amplitude Limiter

Kyle R. H. Bottrill, Francesca Parmigiani, David J. Richardson, Periklis Petropoulos
2017 Journal of Lightwave Technology  
In this work, we use the second stage to perform amplitude squeezing through parametric gain saturation and characterise the scheme by regenerating a QPSK signal contaminated with broadband amplitude noise  ...  Experimental analysis of the system with and without predistortion is provided and phase preserving operation using the proposed scheme is confirmed.  ...  The authors are affiliated with the Optoelectronics Research Centre, University of Southampton, SO17 1BJ, UK.  ... 
doi:10.1109/jlt.2016.2604493 fatcat:kb5zm6puwnhe3gmhgdckys5hxa
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