Adaptive noise-reducing anisotropic diffusion filter

Abdallah, Mariem Ben; Malek, Jihene; Azar, Ahmad Taher; Belmabrouk, Hafedh; Monreal, Julio Esclarín; Krissian, Karl

doi:10.1007/s00521-015-1933-9

Cited by 37 publications

(14 citation statements)

References 104 publications

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“…There were more approaches proposed which use fingerprint models but which are no variation of contextual filtering. Khan et al [53] and Abdallah et al [54] proposed to use anisotropic diffusion as fingerprint images are made of flow-like structures and therefore assumed to be anisotropic. Gottschlich [55] proposed the usage of curved filters and to make use of knowledge about fingerprints.…”

Section: Methods Of Image Enhancementsmentioning

confidence: 99%

Survey on the impact of fingerprint image enhancement

Schuch¹,

Schulz²,

Busch³

2017

IET biom.

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The performance of fingerprint comparison algorithms depends on the reliability and accuracy of the features extracted from the fingerprints. The accuracy of the feature extraction algorithms is assumed to depend on the quality of the fingerprint images. Especially, low-quality images can be challenging for feature extraction algorithms. Image enhancement may allow to extract features more accurately. There is a lack of extensive and quantitative evaluation of image enhancement methods. This study investigates the impact of seven typical image enhancement methods on biometric sample quality and on biometric performance. The interrelation of image quality and biometric performance is investigated on 14 datasets. Biometric quality measures are estimated based on image quality metrics NFIQ1 and NFIQ2.0. Biometric performance is tested using MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. This work shows that the biometric performance can be improved by image enhancement. The significance of improvements depends on both the quality of the datasets and the feature extraction. Thus, there is no single best improvement algorithm. A correlation of changes in scores and image qualities can only be found on the level of entire datasets. No significant correlation can be found for single biometric comparisons.

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Section: Methods Of Image Enhancementsmentioning

confidence: 99%

Survey on the impact of fingerprint image enhancement

Schuch¹,

Schulz²,

Busch³

2017

IET biom.

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“…Abdallah et al [7] proposed a novel Adaptive Noise-Reducing Anisotropic Diffusion (ANRAD) technique for improving the image quality by using a modified Speckle-Reducing Anisotropic Diffusion (SRAD) filter. In this technique, an automatic RGB noise model estimator was introduced to estimate an upper bound of the real noise level function at each iteration by fitting a lower envelope to the standard deviations of pre-segment image variations.…”

Section: Literature Surveymentioning

confidence: 99%

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Indupriya*,

Vijendran

2019

IJITEE

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The most essential process in image processing applications is eliminating the noisy pixels from the images and acquiring the noise-free images with better reconstructed quality. This process has high complexity due to the segmentation of image patches from the noisy images. To simplify this process, Neuro-Fuzzy filtering with Optimum Adaptive parameterized Mask Non-Harmonic Analysis in Curvelet Transform domain (NF-OAMNHA-CT) has been proposed for image denoising. It uses NF edge detector instead of canny edge detector for segmenting and preserving the edge regions including homogeneous texture boundaries with minimized edge distortion. However, it requires gradient information about the error function with respect to the parameter measures during training of NF for optimizing their parameters. Also, it often affects with the fixed local minima. Therefore in this article, Recurrent Polak- Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy (RPCGNF-OAMNHA-CT) technique is proposed to enhance the edge-preserving segmentation in image denoising. At first, the noisy images are defined in the CT domain and fed to the RPCGNF edge detector as edge-preserving segmentation that segments and extracts the edge regions as well as homogeneous texture regions. In this RPCGNF technique, recurrent mechanism is applied to construct PCGNF as an updated edgepreserving segmentation technique that learns their parameters with high efficiency and speeding up the convergence. As a result, the edge regions and homogeneous textures are synchronously segmented as noisy pixels until either the PCGNF converges to the desired accuracy of segmentation or a termination criterion is reached. Further, OAMNHA is applied on each segment to remove the noisy pixels from the images and obtain the noise-free images accurately. Finally, the experimental results illustrate the proposed RPCGNFOAMNHA-CT technique achieves higher efficiency than the NFOAMNHA-CT technique in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE) and Structural Similarity (SSIM).

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“…In fact wavelet analysis is capable of revealing aspects of data that other signal analysis techniques cannot take into account, especially when breakdown points, discontinuities in higher derivatives, and other self-similarity occur. Furthermore, because it let us obtain a different representation of data than those offered by traditional techniques, it can help us to efficiently compress or de-noise a signal without any appreciable degradation [9], [10], [11]. A significant advantage of using wavelets for speech coding is that the compression ratio can easily be optimised, while most other techniques have fixed compression ratios keeping all the other parameters constant.…”

Section: Waveletsmentioning

confidence: 99%

A Real-Time Audio Compression Technique Based on Fast Wavelet Filtering and Encoding

Romano¹,

Scivoletto²,

Połap³

2016

Annals of Computer Science and Information Systems

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Abstract-With the development of telecommunication technology over the last decades, the request for digital information compression has increased dramatically. In many applications, such as high quality audio transmission and storage, the target is to achieve audio and speech signal codings at the lowest possible data rates, in order to offer cheaper costs in terms of transmission and storage. Recently, compression techniques using wavelet transform have received great attention because of their promising compression ratio, signal to noise ratio, and flexibility in representing speech signals. In this paper we examine a new technique for analysing and compressing speech signals using biorthogonal wavelet filters. In particular, we compare this innovative compression method with a typical VoIP encoding of human voice, underlining how using wavelet filters may be convenient, mainly in terms of compression rate, without introducing a significant impairment in signal quality for listeners.

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Adaptive noise-reducing anisotropic diffusion filter

Cited by 37 publications

References 104 publications

Survey on the impact of fingerprint image enhancement

Survey on the impact of fingerprint image enhancement

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

A Real-Time Audio Compression Technique Based on Fast Wavelet Filtering and Encoding

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