Restauration d'images SAR-ERS-1 par une méthode multi-échelle

Le problème de filtrage du bruit de type "speckle" a soulevé l'intérêt de plusieurs chercheurs. L'approche multirésolution a notamment permis d'améliorer les performances des filtres. Cet article introduit une nouvelle technique de filtrage du "speckle", dite "puzzle" qui est adaptative, efficace, simple à implanter et qui ne nécessite ni information sur le bruit ou l'image, ni une détermination de seuils. La méthode consiste à chercher en tout point représentatif de l'image, la version filtrée la plus adéquate prise à partir d'une représentation multi-échelle complète. Les tests effectués sur des images SAR ERS-1 ont donné des résultats dont l'évaluation quantitative et qualitative apparaît satisfaisante

Multiplicative Multiresolution Decomposition for Lossless Volumetric Medical Images Compression

With the emergence of medical imaging, the compression of volumetric medical images is essential. For this purpose, we propose a novel Multiplicative Multiresolution Decomposition (MMD) wavelet coding scheme for lossless compression of volumetric medical images. The MMD is used in speckle reduction technique but offers some proprieties which can be exploited in compression. Thus, as the wavelet transform the MMD provides a hierarchical representation and offers a possibility to realize lossless compression. We integrate in proposed scheme an inter slice filter based on wavelet transform and motion compensation to reduce data energy efficiently. We compare lossless results of classical wavelet coders such as 3D SPIHT and JP3D to the proposed scheme. This scheme incorporates MMD in lossless compression technique by applying MMD/wavelet or MMD transform to each slice, after inter slice filter is employed and the resulting sub-bands are coded by the 3D zero-tree algorithm SPIHT. Lossless experimental results show that the proposed scheme with the MMD can achieve lowest bit rates compared to 3D SPIHT and JP3D

Complexity reduction in the H.264/AVC using highly adaptive fast mode decision based on macroblock motion activity

The H.264/AVC video coding standard is used in a wide range of applications from video conferencing to high-definition television according to its high compression efficiency. This efficiency is mainly acquired from the newly allowed prediction schemes including variable block modes. However, these schemes require a high complexity to select the optimal mode. Consequently, complexity reduction in the H.264/AVC encoder has recently become a very challenging task in the video compression domain, especially when implementing the encoder in real-time applications. Fast mode decision algorithms play an important role in reducing the overall complexity of the encoder. In this paper, we propose an adaptive fast intermode algorithm based on motion activity, temporal stationarity, and spatial homogeneity. This algorithm predicts the motion activity of the current macroblock from its neighboring blocks and identifies temporal stationary regions and spatially homogeneous regions using adaptive threshold values based on content video features. Extensive experimental work has been done in high profile, and results show that the proposed source-coding algorithm effectively reduces the computational complexity by 53.18% on average compared with the reference software encoder, while maintaining the high-coding efficiency of H.264/AVC by incurring only 0.097 dB in total peak signal-to-noise ratio and 0.228% increment on the total bit rate

Compression d'images par "matching pursuit"

Nous présentons une extension au cas 2-D, de l'algorithme de décomposition adaptative multi-échelle "Matching pursuit" de S. Mallat. En utilisant les atomes espace- fréquence de Gabor, et un critère d'arrêt basé sur la norme de l'image résiduelle, un taux de compression de l'ordre de 47.31 est alors obtenu

Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm

International audienceBecause of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter- and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques

Application of BSIF, Log-Gabor and mRMR Transforms for Iris and Palmprint based Bi-modal Identification System

International audienceVerification of individual identity through the process of biometric identification involves comparison between an encoded value and a stored value of the biometric feature in question. The effectiveness of a multimodal user authentication system is greater, but so is its complexity. The system error rate is reduced by the fact that multiple biometric features are combined, thus solving the weakness of the single biometric. Performance of individual authentication through palm-print-and iris-based bi-modal biometric system is proposed in the present study. To this end, Log-Gabor filter and BSIF (Binarised Statistical Image Feature) coefficients are employed to obtain the iris and palm-print traits, and subsequently selection of the features vector is conducted with mRMR (Minimum Redundancy Maximum Relevance) transforms in higher coefficients. To match the iris or palm-print feature vector, the Hamming Distance is applied. According to the experiment outcomes, the proposed system not only has a significantly high recognition rate but it also affords greater security compared to the single biometric system

Superpixel-based Zernike Moments for Palm-print Recognition

International audienceIn the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike Moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47 % on the palm-print PolyU database

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

International audiencePalmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction. © 2017 SPIE and IS&T

Evaluation of No-reference quality metrics for Ultrasound liver images

International audienc