Algorithme de réduction de speckle basé sur le Maximum de Vraisemblance et modélisation des niveaux de gris par un maillage triangulaire continu

- Nous présentons dans cet article une nouvelle méthode de réduction de speckle qui repose sur un algorithme de Maximum de Vraisemblance. Cette méthode a pour particularité d'utiliser un modèle global de l'image, en opposition aux fenêtres glissantes utilisées dans de nombreux algorithmes. Le modèle de l'image est construit à partir d'un maillage triangulaire continu. Une régularisation (en plus de celle introduite par le maillage lui-même) basée sur les lignes de niveaux des valeurs de gris de l'image, est introduite afin d'améliorer la réduction de speckle dans le cas de maillage fin. Une comparaison des performances avec des algorithmes connus est réalisée à l'aide d'images synthétiques. Un résultat sur une image réelle est également présenté

Compression of digital holograms for three-dimensional object reconstruction and recognition

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain

Compression of digital holograms for three-dimensional object reconstruction and recognition

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain

Automated Three-Dimensional Microbial Sensing and Recognition Using Digital Holography and Statistical Sampling

We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition

Neural Network for Three-Dimensional Object Recognition Based on Digital Holography

We present a two-layer neural network to process 3D images which are obtained by digital holography. The network is trained with a real 3D object to compute the weights. Experiments are presented to illustrate the system performance. The system is designed to detect a three-dimensional object in the presence of various distortions. As an example, experiments are presented to illustrate how the system is able to recognize a 3D object with 360° out-of-plane rotation