Spatial resolutions for applying photorefractive polymers to diffractive elements

In this work, we report the reversible reconstruction of holographic and distorted transmission images through the four wave mixing (FWM) technique and optical phase conjugation (OPC), an alternative method to adaptive optics, by using highly efficient Photorefractive (PR) polymers fabricated in our laboratories. These PR polymers are based on our synthesized nonlinear chromophore 4-[4-(diethylamino)-2 hydroxybenzylideneamino] benzonitrile (Dc). For the PR devices, diffraction efficiencies as high as 90% at 25 wt.% doping level of Dc at an external applied electric field (Eext) around 56 V/m are achieved. The reconstruction implementation is simple, of low cost, all-optical and it is capable of recovering 90% of the original images. The real-time holographic experiments were performed at Eext of just 27 V/m, which is one of the lowest reported values. Reversible holographic imaging is showed with a rise-time around 0.35 s. All Rights Reserved © 2015 Universidad Nacional Autonoma de Mexico, Centro de Ciencias Aplicadas y Desarrollo Tecnologico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

There are several situations that simultaneously require both high spatial and spectral resolution in a single image. In literature a certain number of methods have been proposed to merge panchromatic and multispectral data. Classical processes involve a simple linear transformation of the combined data spaces (Brovey, HSV, PCA). However standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they distort the spectral characteristics of the original multispectral data. It has been shown that an image fusion technique based on multiresolution wavelet decomposition (MWD) seems to be a better trade-off between spectral and spatial information. In this paper, we develop some techniques, based on MWD, for the data fusion of such images. The methods presented for reducing the fusion imperfections don't consist simply of replacing some wavelet coefficients of the multispectral image by the corresponding coefficients of the high-resolution image but tend to carry out a real. integration between two pyramidal decomposition. In fact, it is possible to fuse the wavelet coefficients of two decompositions in each scale level and with some appropriate rules. Moreover, some ad hoc algorithms have been implemented to correct spectral distortions in the synthesized high resolution multispectral image. We tested the methods to merge IKONOS panchromatic (1 m spatial resolution) and IKONOS multispectral (4 m spatial resolution) images in urban areas. The techniques presented are clearly better than classical fusion methods in preserving both spectral and spatial information on urban areas.

The present survey gives an introduction into the basics of computed tomography, magnetic resonance tomography and positron emission tomography. The current potentials of these methods in relation to their temporal, spatial and contrast resolutions as well as their sensitivities within clinical routine and experimental studies (in vitro, ex vivo) will be presented. Computed tomography constitutes the anatomical reference method with well defined contrast, high spatial resolution but low sensitivity (10 -2 mol/l) for functional and molecular imaging. Magnetic resonance tomography represents the anatomical method for research with variable tissue contrast, physiological image information, highest spatial resolution but moderate sensitivity (10 -3 -10 -5 mol/l) for functional and molecular imaging. Positron emission tomography offers good suitability for molecular imaging due to highest sensitivity (10 -11 -10 -12 mol/l). However, the spatial resolution of positron emission tomography is low.

There is a need to obtain higher specificity in the detection of breast lesions using MRI. To address this need, Dynamic Contrast-Enhanced (DCE) MRI has been combined with other structural and functional MRI techniques. Unfortunately, owing to time constraints structural images at ultra-high spatial resolution can generally not be obtained during contrast uptake, whereas the relatively low spatial resolution of functional imaging (e.g. diffusion and perfusion) limits the detection of small lesions. To be able to increase spatial as well as temporal resolution simultaneously, the sensitivity of MR detection needs to increase as well as the ability to effectively accelerate the acquisition. The required gain in signal-to-noise ratio (SNR) can be obtained at 7T, whereas acceleration can be obtained with high-density receiver coil arrays. In this case, morphological imaging can be merged with DCE-MRI, and other functional techniques can be obtained at higher spatial resolution, and with less distortion [e.g. Diffusion Weighted Imaging (DWI)]. To test the feasibility of this concept, we developed a unilateral breast coil for 7T. It comprises a volume optimized dual-channel transmit coil combined with a 30-channel receive array coil. The high density of small coil elements enabled efficient acceleration in any direction to acquire ultra high spatial resolution MRI of close to 0.6 mm isotropic detail within a temporal resolution of 69 s, high spatial resolution MRI of 1.5 mm isotropic within an ultra high temporal resolution of 6.7 s and low distortion DWI at 7T, all validated in phantoms, healthy volunteers and a patient with a lesion in the right breast classified as Breast Imaging Reporting and Data System (BI-RADS) IV.

We have investigated photorefractive (PR) properties of a polymer composite with low glass-transition temperature (T g ) in a symmetric reflection geometry. A diffraction efficiency of more than 30% is observed in 105μm thick devices. In low T g photorefractive polymers, poling of the nonlinear optical chromophores at room temperature leads to birefringence in the material. The birefringence will alter the Bragg condition, as the propagation vectors for object and reference beams as well as the readout angle are influenced. We observed the Bragg-mismatch effect that caused a reduction in diffraction efficiency as the external field is increased. We have varied the angle of readout beam slightly at each bias field to get the highest efficiency.

Land surface temperature (LST) images retrieved from the thermal infrared (TIR) band data of Moderate Resolution Imaging Spectroradiometer (MODIS) have much lower spatial resolution than the MODIS visible and near-infrared (VNIR) band data. The coarse pixel scale of MODIS LST images (1000 m under nadir) have limited their capability in applying to many studies required high spatial resolution in comparison of the MODIS VNIR band data with pixel scale of 250–500 m. In this paper we intend to develop an efficient approach for pixel decomposition to increase the spatial resolution of MODIS LST image using the VNIR band data as assistance. The unique feature of this approach is to maintain the thermal radiance of parent pixels in the MODIS LST image unchanged after they are decomposed into the sub-pixels in the resulted image. There are two important steps in the decomposition: initial temperature estimation and final temperature determination. Therefore the approach can be termed double-step pixel decomposition (DSPD). Both steps involve a series of procedures to achieve the final result of decomposed LST image, including classification of the surface patterns, establishment of LST change with normalized difference of vegetation index (NDVI) and building index (NDBI), reversion of LST into thermal radiance through Planck equation, and computation of weights for the sub-pixels of the resulted image. Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with much higher spatial resolution than MODIS data was on-board the same platform (Terra) as MODIS for Earth observation, an experiment had been done in the study to validate the accuracy and efficiency of our approach for pixel decomposition. The ASTER LST image was used as the reference to compare with the decomposed LST image. The result showed that the spatial distribution of the decomposed LST image was very similar to that of the ASTER LST image with a root mean square error (RMSE) of 2.7 K for entire image. Comparison with the evaluation DisTrad (E-DisTrad) and re-sampling methods for pixel decomposition also indicate that our DSPD has the lowest RMSE in all cases, including urban region, water bodies, and natural terrain. The obvious increase in spatial resolution remarkably uplifts the capability of the coarse MODIS LST images in highlighting the details of LST variation. Therefore it can be concluded that, in spite of complicated procedures, the proposed DSPD approach provides an alternative to improve the spatial resolution of MODIS LST image hence expand its applicability to the real world.

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method

An acoustical imaging could be numerically reconstructed by several methods, one of which is Backward Propagation method. This method has many advantages especially it could be used in nearer distance where it is no longer in the region of Fresnel diffraction. Using traditional backward propagation method, the lateral resolution of the imaging system would be constrained by spatial sampling interval when the imaging distance is very short. This problem is discussed and the solution worked out using the method of data augmenting in spatial frequency domain. The details of the method and the results of computer simulant experiments are given in the paper.

This paper proposes a novel framework for image fusion of satellite imagery to enhance spatial resolution of the image via structure-texture decomposition. The resolution of the satellite imagery depends on the sensors, for example, panchromatic images have high spatial resolution but only a single gray band whereas multi-spectral images have low spatial resolution but multiple bands. To enhance the spatial resolution of low-resolution images, such as multi-spectral or infrared images, the proposed framework combines the structures from the low-resolution image and the textures from the high-resolution image. To improve the spatial quality of structural edges, the structure image from the low-resolution image is guided filtered with the structure image from the high-resolution image as the guidance image. The combination step is performed by pixel-wise addition of the filtered structure image and the texture image. Quantitative and qualitative evaluation demonstrate the proposed method preserves spectral and spatial fidelity of input images.

Magnetic resonance imaging (MRI) has emerged as a powerful tool in medical diagnosis and research. Although high spatial resolution images are essential in medical diagnosis and image analysis, high temporal resolution is equally important in applications of dynamic contrast-enhanced MRI or functional brain MRI. In particular, in breast MRI the ability to differentiate between benign and malignant lesions depends, in part, on the temporal resolution of the dynamic image acquisition. New applications of MRI such as multi-feature analysis of image time series data and full 3D functional MRI or event-related functional MRI require high spatial and high temporal resolution for accurate image analysis on a voxel-by-voxel basis. Currently available partial Fourier reconstruction techniques, which effectively improve the time resolution, suffer from a reduced signal to noise ratio in the reconstructed image, a decrease in spatial resolution or reconstruction artefacts, making numerical image analysis difficult. In this work we present an image reconstruction algorithm based on image recovery theory which effectively doubles the temporal resolution and results in an image quality sufficient for further numerical analysis. The developed algorithm requires a full Fourier space acquisition of a pre-contrast or baseline image prior to the reconstruction procedure of the time series partial Fourier data.

We report on high-resolution photorefractive polymer-dispersed liquid crystals that lead to high diffraction efficiency at lower applied fields compared with photorefractive polymers. The diffraction efficiency reaches 100% internally for 105-μm-thick samples at a field of 8 V/μm. Net optical gain and diffraction efficiencies of 56% are demonstrated in 53-μm-thick devices. Subsecond response times have also been obtained using polymeric matrices with good transport properties.

Cystic Adventitial Disease and High Spatial Resolution Magnetic Resonance Imaging

As a branch of Internet of Things (IoT), network geographic information (WebGIS) has the task of enhancing remote sensing images, including correction, fusion, mapping, etc. Remote sensing images captured by different satellite platforms often present earth objects with various scales. Due to the different spatial resolutions of imaging sensors, the same object will appear in different sizes. Usually panchromatic (PAN) image, without spectral diversity, has high spatial resolution and multispectral (MS) image has lower spatial resolution but more spectral information. Fusing MS and PAN images, also known as pansharpening, will increase the spatial resolution of MS image without changing its spectral information. In the fusion process, inaccurate perception and extraction of the spatial details will lead to spectral and spatial distortions in the fused image. A new multi-scale structure perception based fusion method is proposed in this paper. Firstly, an improved structure-preserving filter is designed by using MS imagery as the reference to capture the structures in the PAN image. Then the structures having different scales are progressively perceived through a multi-scale decomposition scheme and the spatial details are accurately extracted from the PAN image. Finally, the details weighted by the band-dependent gains are injected into each band of MS images. Experimental results are compared with some recently proposed edge-preserving filtering based fusion methods. Visual analysis and objective evaluations demonstrate that the proposed method can produce high-quality fused output and achieve better performance than some state-of-the-arts.KeywordsFusionRemote sensingFilterMultispectral image

Photoacoustic imaging combines advantages of both ultrasound imaging and optical imaging to provide high ultrasonic resolution and high optical contrast in noninvasive medical images. A photoacoustic computer tomography （PCT） system with a focused digital phased array transducer is proposed. In particular each element of the transducer array has a thin cylindrical ultrasonic lens to select 2D image plane and suppress the out-of-plane signals to realize photoacoustic computed tomography. A fast full-view photoacoustic imaging system for visualizing tissue structures using a linear transducer array with combined scan, whies; detects photoacoustic signals by combined electronic and mechanical scanning. An improved limited-field filtered back projection algorithm with directivity factors was applied to reconstruct the optical absorption distribution and further improve the lateral resolution. In the experiment, the spatial resolution of this imaging system is determined as 0.2mm, and the slice resolution of Z axis is 1.5mm. Mechanical step scanning was performed, leading to a measurement time of 6.4 seconds per position, and a total scan duration of 150 seconds to obtain the two-dimensional photoacoustic image. High spatial resolution of 2D photoacoustic vascular image and quasi-3D photoacoustic CT for breast cancer imaging are achicned. The experimental results demonstrate that this method and imaging system can realize the high resolution photoacoustic tomography wtich can be applied to noninvasive detection of early breast cancer.

PurposeTo improve the spatial resolution and repeatability of a non-contrast MRI technique for simultaneous time resolved 3D angiography and perfusion imaging by developing an efficient 3D cone trajectory design.MethodsA novel parameterized 3D cone trajectory design incorporating the 3D Golden Angle was integrated into 4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) to achieve higher spatial resolution and sampling efficiency for both dynamic angiography and perfusion imaging with flexible spatiotemporal resolution. Numerical simulations and physical phantom scanning were used to optimize the cone design. Eight healthy volunteers were scanned to compare the original radial trajectory in 4D CAPRIA with our newly designed cone trajectory. A locally low rank reconstruction method was used to leverage the complementary k-space sampling across time.ResultsThe improved sampling in the periphery of k-space obtained with the optimized 3D cone trajectory resulted in improved spatial resolution compared with the radial trajectory in phantom scans. Improved vessel sharpness and perfusion visualization were also achieved in vivo. Less dephasing was observed in the angiograms due to the short echo time of our cone trajectory and the improved k-space sampling efficiency also resulted in higher repeatability compared to the original radial approach.ConclusionThe proposed 3D cone trajectory combined with 3D Golden Angle ordering resulted in improved spatial resolution and image quality for both angiography and perfusion imaging and could potentially benefit other applications that require an efficient sampling scheme with flexible spatial and temporal resolution.