Simple Deconvolution Research Articles

PARTIAL VOLUME CORRECTION TECHNIQUES FOR LONGITUDINAL TAU‐PET QUANTIFICATION: A COMPARISON STUDY

AbstractBackgroundThe selection of an appropriate Partial Volume Correction (PVC) is crucial for evaluating tau accumulation rates using 18F‐Flortaucipir (18F‐FTP). PVC can reduce the impact of off‐target binding, but it can also increase noise and variability. While these effects may be averaged on cross‐sectional studies, they might be of paramount importance when evaluating within‐subject longitudinal tau‐PET signal. The aim of this study is to evaluate the robustness of different PVCs for the evaluation of tau accumulation rates in longitudinal 18F‐FTP studies, as measured by their correlation with cognitive clinical progression.Method111 cognitively impaired subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with serial 18F‐FTP PET and T1 images, as well as cognitive performance evaluation available for a 2‐year follow‐up period, were included in the study. 18F‐FTP PET images were corrected by applying a traditional deconvolution PVC (Grey matter only), the reblurred Van‐Cittert (RVC) analytical PVC (grey + white matter), and 2 voxel‐based PVCs, the region‐based voxel‐wise correction (RBV) and the iterative Yang (iY). Longitudinal rates of change for the regional standard uptake value ratios (SUVR) of the temporal meta‐ROI were computed for the images corrected for the different PVCs, and their correlation with the rates of change of the ADAS‐Cog 11 cognitive performance metric was analyzed.ResultThe deconvolution PVC correction showed the strongest correlation between decreased cognitive performance and increased tau accumulation rates (p<0.001, ρ = 0.38), followed closely by the RBV (p<0.001, ρ = 0.36) and iY (p<0.001, ρ = 0.35) corrections (Fig. 1). In contrast, the RVC results (p = 0.03, ρ = 0.20) were comparable to not applying any PVC at all (p = 0.03, ρ = 0.21).ConclusionOur findings suggest that longitudinal FTP studies might benefit from the use of PVC techniques for the evaluation of tau accumulation rates. Tau uptake rates of change tracked the observed clinical progression more accurately after certain PVC corrections were applied. Moreover, a simple deconvolution PVC has been shown to perform similarly to more complex, voxel‐wise corrections such as the RBV and iY.

Investigation of Hole-Transfer Dynamics through Simple EL De-Convolution in Non-Fullerene Organic Solar Cells.

In conventional fullerene-based organic photovoltaics (OPVs), in which the excited electrons from the donor are transferred to the acceptor, the electron charge transfer state (eECT) that electrons pass through has a great influence on the device's performance. In a bulk-heterojunction (BHJ) system based on a low bandgap non-fullerene acceptor (NFA), however, a hole charge transfer state (hECT) from the acceptor to the donor has a greater influence on the device's performance. The accurate determination of hECT is essential for achieving further enhancement in the performance of non-fullerene organic solar cells. However, the discovery of a method to determine the exact hECT remains an open challenge. Here, we suggest a simple method to determine the exact hECT level via deconvolution of the EL spectrum of the BHJ blend (ELB). To generalize, we have applied our ELB deconvolution method to nine different BHJ systems consisting of the combination of three donor polymers (PM6, PBDTTPD-HT, PTB7-Th) and three NFAs (Y6, IDIC, IEICO-4F). Under the conditions that (i) absorption of the donor and acceptor are separated sufficiently, and (ii) the onset part of the external quantum efficiency (EQE) is formed solely by the contribution of the acceptor only, ELB can be deconvoluted into the contribution of the singlet recombination of the acceptor and the radiative recombination via hECT. Through the deconvolution of ELB, we have clearly decided which part of the broad ELB spectrum should be used to apply the Marcus theory. Accurate determination of hECT is expected to be of great help in fine-tuning the energy level of donor polymers and NFAs by understanding the charge transfer mechanism clearly.

Inaccuracies and biases of the Gaussian size deconvolution for extracted sources and filaments

A simple Gaussian size deconvolution method is routinely used to remove the blur of observed images caused by insufficient angular resolutions of existing telescopes, thereby to estimate the physical sizes of extracted sources and filaments. To ensure that the physical conclusions derived from observations are correct, it is necessary to know the inaccuracies and biases of the size deconvolution method, which is expected to work when the structures, as well as the telescope beams, have Gaussian shapes. This study employed model images of the spherical and cylindrical objects with Gaussian and power-law shapes, representing the dense cores and filaments observed in star-forming regions. The images were convolved to a wide range of angular resolutions to probe various degrees of resolvedness of the model objects. Simplified shapes of the flat, convex, and concave backgrounds were added to the model images, then planar backgrounds across the footprints of the structures are subtracted and sizes of the sources and filaments were measured and deconvolved. When background subtraction happens to be inaccurate, the observed structures acquire profoundly non-Gaussian profiles. The deconvolved half maximum sizes can be strongly under- or overestimated, by factors of up to ~20 when the structures are unresolved or partially resolved. For resolved structures, the errors are generally within a factor of ~2; although, the deconvolved sizes can be overestimated by factors of up to ~6 for some power-law models. The results show that Gaussian size deconvolution cannot be applied to unresolved structures, whereas it can only be applied to the Gaussian-like structures, including the critical Bonnor-Ebert spheres, when they are at least partially resolved. The deconvolution method must be considered inapplicable for the power-law sources and filaments with shallow profiles. This work also reveals subtle properties of convolution for structures of different geometry. When convolved with different kernels, spherical objects and cylindrical filaments with identical profiles obtain different widths and shapes. In principle, a physical filament, imaged by the telescope with a non-Gaussian point-spread function, could appear substantially shallower than the structure is in reality, even when it is resolved.

Influence of Cerium oxide on the structural aspects of soda-lime aluminosilicate glasses synthesized by sol-gel route

Soda-lime aluminosilicate glasses containing different mole fractions of cerium oxide (1–10 mol.%) with a nominal composition 2xCeO2-45SiO2-15Al2O3-(20-x)Na2O-(20-x)CaO were successfully synthesized via sol-gel route. Structural and role of cerium addition were investigated via X-ray diffraction (XRD), Fourier transforms infrared (FT-IR), Raman spectroscopic, and Photoluminescence techniques. All investigated glasses show a broad diffraction band (amorphous halo) pointing to the long-range structural disorder and amorphous nature of the as-synthesized glass powder. Raman and Fourier transform infrared spectral data indicate that CeO2 plays a crucial role (network modifier) in the main soda-lime aluminosilicate network. The building groups were Qn retraced by a simple deconvolution analysis technique (DAT) and the exchange among Q3 and Q2 species can be described simply in terms of interchange in the former arrangements Si2O52− (Q3) → SiO32− (Q2), this exchange occurs with high concentrations of cerium oxide in glasses matrix. Undoped and doped soda-lime aluminosilicate photoluminescence characteristics were analyzed. PL spectrum of the undoped sample reveals significant bands caused by Na, Ca, and Al ions in silicate glasses; once the doped cerium ion was added to the glasses, two bands formed at 690 and 538 nm, which are attributed to the well-known 5d-4f cerium emission band. Computational density functional theory (DFT) was applied for a single building block of the glass network using Becke three-parameter hybrid functional (B3LYP) correlation adopting the electron core potential basis set (3-21G) was used.

Wave-optics-based image synthesis for super resolution reconstruction of a FZA lensless camera.

A Fresnel Zone Aperture (FZA) mask for a lensless camera, an ultra-thin and functional computational imaging system, is beneficial because the FZA pattern makes it easy to model the imaging process and reconstruct captured images through a simple and fast deconvolution. However, diffraction causes a mismatch between the forward model used in the reconstruction and the actual imaging process, which affects the recovered image's resolution. This work theoretically analyzes the wave-optics imaging model of an FZA lensless camera and focuses on the zero points caused by diffraction in the frequency response. We propose a novel idea of image synthesis to compensate for the zero points through two different realizations based on the linear least-mean-square-error (LMSE) estimation. Results from computer simulation and optical experiments verify a nearly two-fold improvement in spatial resolution from the proposed methods compared with the conventional geometrical-optics-based method.

MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation

In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multi-scale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration.

DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation

Background and Objective: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some drawbacks. Due to the significant loss of spatial information at the coding stage, it is often difficult to restore the details of low-level visual features using simple deconvolution, and the generated feature maps are sparse, which results in performance degradation. This prompted us to study whether it is possible to better preserve the deep feature information of the image in order to solve the sparsity problem of image segmentation models. Methods: In this study, we (1) build a reliable deep learning network framework, named DCACNet, to improve the segmentation performance for medical images; (2) propose a multiscale cross-fusion encoding network to extract features; (3) build a dual context aggregation module to fuse the context features at different scales and capture more fine-grained deep features; and (4) propose an attention-guided cross deconvolution decoding network to generate dense feature maps. We demonstrate the effectiveness of the proposed method on two publicly available datasets. Results: DCACNet was trained and tested on the prepared dataset, and the experimental results show that our proposed model has better segmentation performance than previous models. For 4-class classification (CHAOS dataset), the mean DSC coefficient reached 91.03%. For 2-class classification (Herlev dataset), the accuracy, precision, sensitivity, specificity, and Dice score reached 96.77%, 90.40%, 94.20%, 97.50%, and 97.69%, respectively. The experimental results show that DCACNet can improve the segmentation effect for medical images. Conclusion: DCACNet achieved promising results on the prepared dataset and improved segmentation performance. It can better retain the deep feature information of the image than other models and solve the sparsity problem of the medical image segmentation model.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

Wavelet deconvolution technique for impact force reconstruction: mutual deconvolution approach

In the field of impact engineering, one of the most concerned issues is how to exactly know the history of impact force which often difficult or impossible to be measured directly. In reality, information of impact force apply to structure can be identified by means of indirect method from using information of corresponding output responses measured on structure. Namely, by using the output responses (caused by the unknown impact force) such as acceleration, displacement, or strain, etc. in cooperation with the impulse response function, the profile of unknown impact force can be rebuilt. A such indirect method is well known as impact force reconstruction or impact force deconvolution technique. Unfortunately, a simple deconvolution technique for reconstructing impact force has often encountered difficulty due to the ill-posed nature of inversion. Deconvolution technique thus often results in unexpected reconstruction of impact force with the influences of unavoidable errors which is often magnified to a large value in reconstructed result. This large magnification of errors dominates profile of desired impact force. Although there have been some regularization methods in order to improve this ill-posed problem so far, most of these regularizations are considered in the whole-time domain, and this may make the reconstruction inefficient and inaccurate because impact force is normally limited to some portions of impact duration. This work is concerned with the development of deconvolution technique using wavelets transform. Based on the advantages of wavelets (i.e., localized in time and the possibility to be analyzed at different scales and shifts), the mutual reconstruction process is proposed and formulated by considering different scales of wavelets. The experiment is conducted to verify the proposed technique. Results demonstrated the robustness of the present technique when reconstructing impact force with more stability and higher accuracy.

SiCoDeF² Net: Siamese Convolution Deconvolution Feature Fusion Network for One-Shot Classification

Nowadays, deep convolutional neural networks (CNNs) for face recognition exhibit a performance comparable to human ability in the presence of the appropriate amount of labelled training data. However, training CNNs remains as an arduous task due to the lack of training samples. To overcome this drawback, applications demand one-shot learning to improve the obtained performances over traditional machine learning approaches by learning representative information about data categories from few training samples. In this context, Siamese convolutional network ( SiConvNet ) provides an interesting deep architecture to tackle the data limitation. In this regard, applying the convolution operation on real world images by using the trainable correlative Gaussian kernel adds correlations to the output images, which hinder the recognition process due to the blurring effects introduced by the convolution kernel application. As a result the pixel-wise and channel-wise correlations or redundancies could appear in both single and multiple feature maps obtained by a hidden layer. In this sense, convolution-based models fail to generalize the feature representation because of both the strong correlations presence in neighboring pixels and the channel-wise high redundancies between different channels of the feature maps, which hamper the effective training. Deconvolution operation helps to overcome the shortcomings that limit the conventional SiConvNet performance, learning successfully correlation-free features representation. In this paper, a simple but efficient Siamese convolution deconvolution feature fusion network ( SiCoDeF 2 Net ) is proposed to learn the invariant and discriminative complementary features generated from both the (i) sub-convolution (SCoNet) and (ii) sub deconvolutional (SDeNet) networks using a concatenation operation which significantly improves the one-shot unconstrained facial recognition task. Extensive experiments performed on several widely used benchmarks, provide promising results, where the proposed SiCoDeF 2 Net model significantly outperforms the current state-of-art in terms of classification accuracy, F1, precision and recall. The code will be available on: https://github.com/purbayankar/SiCoDeF2Net .

Intrinsic image decomposition as two independent deconvolution problems

In this paper, a novel method to decompose an image into “intrinsic images” is introduced. This decomposition is based on the dichromatic model which separates the influence of specular and diffuse reflections. The separation of these components is very important in several applications of image analysis including segmentation, classification, recoloring, and specularity removal. The proposed method is based on two simple deconvolution steps. The method aims to be generic: it is applicable to any kind of image (i.e. RGB as well as multispectral) and does not rely on a learning step. The method is applied to three datasets including multispectral and RGB images. The algorithm is compared to recent algorithms from the literature and gives similar or better results than the state of the art.

Nanoscale dielectric grating polarizers tuned to 4.43 eV for ultraviolet polarimetry.

Transmissive dielectric wire grid polarizers tuned to 4.43 eV (Mg II line, 280 nm), an important diagnostic line for solar physics, are presented in this communication. The polarizers are based on TiO2 gratings and designed with a period of ∼140 nm (7143 lines/mm), 40 nm line width (duty cycle of 0.286), and 100 nm line height. Several gratings are fabricated through electron beam lithography combined with reactive ion etching, whereby two parameters in the nanofabrication process are explored: e-beam dosage on the photoresist and TiO2 etching time. Polarization of samples is optically characterized using a spectroscopic ellipsometer in transmission mode, achieving the best result with an extinction ratio of ∼109 and a transmittance of 16.4% at the target energy of 4.43 eV. The shape of the gratings is characterized through atomic force microscopy (AFM) and scanning electron microscopy (SEM); the measured AFM profiles are distorted by the tip geometry, hence a simple deconvolution procedure is implemented to retrieve the real profile. By analysing the AFM and SEM profiles, we find that the real shapes of the different gratings are close to the design, but with a larger duty cycle than the intended value. With the real grating geometry, an improved model of the best sample was built with a finite-difference time-domain (FDTD) method that matches the result obtained through optical characterization.

Separation of carbohydrate isomers and anomers on poly-N-(1H-tetrazole-5-yl)-methacrylamide-bonded stationary phase by hydrophilic interaction chromatography as well as determination of anomer interconversion energy barriers

A new commercially available HPLC column, poly-N-(1H-tetrazole-5-yl)-methacrylamide-bonded stationary phase (Daicel DCpak PTZ), was systematically evaluated for its carbohydrate isomer separation capability by hydrophilic interaction liquid chromatography (HILIC) with charged aerosol detection (CAD) or (tandem) mass spectrometry. Reducing sugars tend to split into two anomer peaks which makes carbohydrate isomer separations in non-derivatized form even more complicated. For practical purposes anomer separations are therefore ideally suppressed which can be accomplished by using high temperature or high pH that are both associated with fast interconversion kinetics leading to peak coalescence, or on the other hand by conditions with low chromatographic anomer selectivity. Four major hexoses (glucose, mannose, galactose, fructose), five main pentoses (ribose, ribulose, xylose, xylulose, arabinose) and five most important disaccharides (maltose, cellobiose, lactose, sucrose, trehalose) were analyzed as single carbohydrate standards by isocratic HILIC with 0.1% (v/v) formic acid and 2 mM ammonium acetate at various temperatures to study anomer interconversion equilibria in a pH-dependent manner. Rate constants of forward (α→β) and backward (β→α) anomerization and corresponding energy barriers were calculated. The energy barriers of anomerisation were in the range of around 83–91 kJ mol−1 at 298 K and the difference between forward (α→β) and backward reaction (β→α) was typically between 1–3 kJ mol−1. The systematic studies finally allowed to pick conditions for the simultaneous analysis of all 14 carbohydrates by HILIC-ESI-MS(/MS) with PTZ in gradient elution mode. A combination of carbohydrate isomer-selective LC (with PTZ), tandem MS (with carbohydrate group-selective MS1 and some species-specific SRM transitions) and a simple deconvolution strategy allowed the determination of all carbohydrates of the complex test mixture except for the disaccharide pair lactose and maltose (which can be determined as sum). Consequently, the proposed method represents a successful step towards a global glycometabolomics profiling method of mono- and disaccharides by HILIC-ESI-MS/MS.

Photometric limits on the high resolution imaging of exoplanets using the solar gravity lens

We present an analysis of high-resolution imaging of an exoplanet by a meter-class telescope positioned at a real image of the exoplanet created by the solar gravity lens. We assume an exoplanet viewed in full phase and a simple deconvolution method to correct for the intrinsic blur caused by aberrations in the solar gravity lens, and account for the foreground light of the solar corona. We derive equations for the measurement times required for the telescope to produce such a high-resolution image, and find that due to shot noise considerations these times are large compared to human lifespans. We also discuss how measurement times could potentially be significantly shorter for exoplanets with special orbital and rotational parameters.

Robustness of Spike Deconvolution for Neuronal Calcium Imaging.

Calcium imaging is a powerful method to record the activity of neural populations in many species, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology and found that simple non-negative deconvolution (NND) outperformed all other algorithms on out-of-sample test data. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on “zoomed out” datasets of ∼10,000 cell recordings from the visual cortex of mice of either sex. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND because of its simplicity, efficiency, and accuracy.SIGNIFICANCE STATEMENT The experimental method that currently allows for recordings of the largest numbers of cells simultaneously is two-photon calcium imaging. However, use of this powerful method requires that neuronal firing times be inferred correctly from the large resulting datasets. Previous studies have claimed that complex supervised learning algorithms outperform simple deconvolution methods at this task. Unfortunately, these studies suffered from several problems and biases. When we repeated the analysis, using the same data and correcting these problems, we found that simpler spike inference methods perform better. Even more importantly, we found that supervised learning methods can introduce artifactual structure into spike trains, which can in turn lead to erroneous scientific conclusions. Of the algorithms we evaluated, we found that an extremely simple method performed best in all circumstances tested, was much faster to run, and was insensitive to parameter choices, making incorrect scientific conclusions much less likely.

Development of wavelet deconvolution technique for impact force reconstruction: Application to reconstruction of impact force acting on a load-cell

Deconvolution technique has become a useful solution for indirect measurement of impact force, however, a simple deconvolution technique often encounters difficulty and leads to an unsatisfactory reconstruction due to unavoidable error and the ill-posed nature of the inverse problem. This paper is concerned with a deconvolution technique using wavelet approach for reconstructing impact force. Based on the ability of wavelet transform, impact force reconstruction was formulated and implemented through its expansion coefficients of different scaled and shifted versions of wavelets. The reconstruction of impact force thus becomes the reconstruction of its expansion coefficients which are then used to reproduce the profile of impact force. Considerations of appropriate scales and shifts of wavelets in reconstruction can be regarded as a kind of regularization in order to improve the ill-posed problem. The numerical verification on reconstruction of impact force acting on a load-cell during the impact buckling of thin-walled column showed that the present technique is indeed capable to mitigate the ill-posed nature when the scale level of wavelets is controlled properly. The result also revealed that with an appropriate choice of the shifting parameter at a certain scale level, the noise in reconstructed force has been suppressed further. As a consequence, it has been verified that the present wavelet deconvolution technique can successfully reconstruct the impact force with less noise and higher accuracy in comparison with the conventional methods.

Two-photon speckle illumination for super-resolution microscopy.

We present a numerical study of a microscopy setup in which the sample is illuminated with uncontrolled speckle patterns and the two-photon excitation fluorescence is collected on a camera. We show that, using a simple deconvolution algorithm for processing the speckle low-resolution images, this wide-field imaging technique exhibits resolution significantly better than that of two-photon excitation scanning microscopy or one-photon excitation bright-field microscopy.

A Deconvolution Protocol for ChIP-Seq Reveals Analogous Enhancer Structures on the Mouse and Human Ribosomal RNA Genes.

The combination of Chromatin Immunoprecipitation and Massively Parallel Sequencing, or ChIP-Seq, has greatly advanced our genome-wide understanding of chromatin and enhancer structures. However, its resolution at any given genetic locus is limited by several factors. In applying ChIP-Seq to the study of the ribosomal RNA genes, we found that a major limitation to resolution was imposed by the underlying variability in sequence coverage that very often dominates the protein–DNA interaction profiles. Here, we describe a simple numerical deconvolution approach that, in large part, corrects for this variability, and significantly improves both the resolution and quantitation of protein–DNA interaction maps deduced from ChIP-Seq data. This approach has allowed us to determine the in vivo organization of the RNA polymerase I preinitiation complexes that form at the promoters and enhancers of the mouse (Mus musculus) and human (Homo sapiens) ribosomal RNA genes, and to reveal a phased binding of the HMG-box factor UBF across the rDNA. The data identify and map a “Spacer Promoter” and associated stalled polymerase in the intergenic spacer of the human ribosomal RNA genes, and reveal a very similar enhancer structure to that found in rodents and lower vertebrates.

PROPOSAL OF A GEOSTATISTICAL PROCEDURE FOR TRANSPORTATION PLANNING FIELD

Abstract: The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

Computational imaging through chromatic aberration corrected simple lenses

Modern camera lenses become increasingly more complex to optimize the light efficiency of the optical system. Recent research has combined single lens optics with complex post-capture correction methods based on computational photography. This study further improves lens design by correcting chromatic aberrations, after which a simple image deconvolution method is sufficient to produce a high-quality image. We initially estimate the point spread function with a blind image deconvolution method. We add Gaussian regularization as kernel prior to improve the accuracy of kernel estimation. A fast non-blind deconvolution method is then performed to recover a clear image. Experimental results show that the proposed method is at par with state-of-the-art deconvolution approaches. We found a better trade-off between lens design and digital image processing than previous work.