Facial selectivities in the nucleophilic additions of 2,3-unsaturated 3-arylsulfinyl pyranosides

2,3-Unsaturated 3-arylsulfinyl pyranosides undergo nucleophilic additions at C-2, with facial selectivities depending on the nucleophile and the substituent on sulfinyl sulfur. The reactions of such sugar vinyl sulfoxides lead to the addition of nucleophile preferring an axial orientation at C-2, with concomitant formation of an allylic bond at C-3 to C-4. This trend in the addition pattern is observed for primary amine, carbon and sulfur nucleophiles, whereas secondary amines prefer an equatorial addition at C-2. The effect of p-tolylthio-versus (p-isopropylphenyl)thio vinyl sulfoxide is that the equatorial nucleophilic addition is preferred even more with the latter vinyl sulfoxide. (C) 2013 Published by Elsevier Ltd.

Deconvolution-based deblurring of reconstructed images in photoacoustic/thermoacoustic tomography

Photoacoustic/thermoacoustic tomography is an emerging hybrid imaging modality combining optical/microwave imaging with ultrasound imaging. Here, a k-wave MATLAB toolbox was used to simulate various configurations of excitation pulse shape, width, transducer types, and target object sizes to see their effect on the photoacoustic/thermoacoustic signals. A numerical blood vessel phantom was also used to demonstrate the effect of various excitation pulse waveforms and pulse widths on the reconstructed images. Reconstructed images were blurred due to the broadening of the pressure waves by the excitation pulse width as well as by the limited transducer bandwidth. The blurring increases with increase in pulse width. A deconvolution approach is presented here with Tikhonov regularization to correct the photoacoustic/thermoacoustic signals, which resulted in improved reconstructed images by reducing the blurring effect. It is observed that the reconstructed images remain unaffected by change in pulse widths or pulse shapes, as well as by the limited bandwidth of the ultrasound detectors after the use of the deconvolution technique. (C) 2013 Optical Society of America

High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio

Four-dimensional fluorescence microscopy-which records 3D image information as a function of time-provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

On the images of Sobolev spaces under the heat kernel transform on the Heisenberg group

The aim of this paper is to obtain certain characterizations for the image of a Sobolev space on the Heisenberg group under the heat kernel transform. We give three types of characterizations for the image of a Sobolev space of positive order H-m (H-n), m is an element of N-n, under the heat kernel transform on H-n, using direct sum and direct integral of Bergmann spaces and certain unitary representations of H-n which can be realized on the Hilbert space of Hilbert-Schmidt operators on L-2 (R-n). We also show that the image of Sobolev space of negative order H-s (H-n), s(> 0) is an element of R is a direct sum of two weighted Bergman spaces. Finally, we try to obtain some pointwise estimates for the functions in the image of Schwartz class on H-n under the heat kernel transform. (C) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography

The model-based image reconstruction approaches in photoacoustic tomography have a distinct advantage compared to traditional analytical methods for cases where limited data is available. These methods typically deploy Tikhonov based regularization scheme to reconstruct the initial pressure from the boundary acoustic data. The model-resolution for these cases represents the blur induced by the regularization scheme. A method that utilizes this blurring model and performs the basis pursuit deconvolution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods via three numerical experiments. Moreover, this deconvolution including the building of an approximate blur matrix is achieved via the Lanczos bidagonalization (least-squares QR) making this approach attractive in real-time. (C) 2014 Optical Society of America

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.

Directional bilateral filters for smoothing fluorescence microscopy images

Images obtained through fluorescence microscopy at low numerical aperture (NA) are noisy and have poor resolution. Images of specimens such as F-actin filaments obtained using confocal or widefield fluorescence microscopes contain directional information and it is important that an image smoothing or filtering technique preserve the directionality. F-actin filaments are widely studied in pathology because the abnormalities in actin dynamics play a key role in diagnosis of cancer, cardiac diseases, vascular diseases, myofibrillar myopathies, neurological disorders, etc. We develop the directional bilateral filter as a means of filtering out the noise in the image without significantly altering the directionality of the F-actin filaments. The bilateral filter is anisotropic to start with, but we add an additional degree of anisotropy by employing an oriented domain kernel for smoothing. The orientation is locally adapted using a structure tensor and the parameters of the bilateral filter are optimized for within the framework of statistical risk minimization. We show that the directional bilateral filter has better denoising performance than the traditional Gaussian bilateral filter and other denoising techniques such as SURE-LET, non-local means, and guided image filtering at various noise levels in terms of peak signal-to-noise ratio (PSNR). We also show quantitative improvements in low NA images of F-actin filaments. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise

We propose optimal bilateral filtering techniques for Gaussian noise suppression in images. To achieve maximum denoising performance via optimal filter parameter selection, we adopt Stein's unbiased risk estimate (SURE)-an unbiased estimate of the mean-squared error (MSE). Unlike MSE, SURE is independent of the ground truth and can be used in practical scenarios where the ground truth is unavailable. In our recent work, we derived SURE expressions in the context of the bilateral filter and proposed SURE-optimal bilateral filter (SOBF). We selected the optimal parameters of SOBF using the SURE criterion. To further improve the denoising performance of SOBF, we propose variants of SOBF, namely, SURE-optimal multiresolution bilateral filter (SMBF), which involves optimal bilateral filtering in a wavelet framework, and SURE-optimal patch-based bilateral filter (SPBF), where the bilateral filter parameters are optimized on small image patches. Using SURE guarantees automated parameter selection. The multiresolution and localized denoising in SMBF and SPBF, respectively, yield superior denoising performance when compared with the globally optimal SOBF. Experimental validations and comparisons show that the proposed denoisers perform on par with some state-of-the-art denoising techniques. (C) 2015 SPIE and IS&T

Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.