In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot ...or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.
Aperiodic, clustered-dot, halftone patterns have recently become popular for commercial printing of continuous-tone images with laser, electrophotographic presses, because of their inherent stability ...and resistance to moiré artifacts. Halftone screens designed using the multistage, multipass, clustered direct binary search (MS-MP-CLU-DBS) algorithm can yield halftone patterns with very high visual quality. But the characteristics of these halftone patterns depend on three input parameters for which there are no known formulas to choose their values to yield halftone patterns of a certain quality level and scale. Using machine learning methods, two predictors are developed that take as input these three parameters. One predicts the quality level of the halftone pattern. The other one predicts the scale of the halftone pattern. To provide ground truth information for training these predictors, human subjects viewed a large number of halftone patches generated from MS-MP-CLU-DBS-designed screens and assigned each patch to one of four quality levels. For each patch, the location of the peak in the radially averaged power spectrum (RAPS) is calculated as a measure of the scale or effective line frequency of the pattern. Experimental results demonstrate the accuracy of the two predictors and the effectiveness of screen design procedures based on these predictors to generate both monochrome and color high quality halftone images.
Foodborne pathogens are major public health concerns worldwide. Paper-based microfluidic devices are versatile, user friendly and low cost. We report a novel paper-based single input channel ...microfluidic device that can detect more than one whole-cell foodborne bacteria at the same time, and comes with quantitative reading via image analysis. This microfluidic paper-based multiplexed aptasensor simultaneously detects E. coli O157:H7 and S. Typhimurium. Custom designed particles provide colorimetric signal enhancement and false results prevention. Several aptamers were screened and the highest-affinity aptamers were optimized and employed for detection of these bacteria in solution, both in a buffer as well as pear juice. Image analysis was used to read and quantify the colorimetric signal and measure bacteria concentration, thus rendering this paper based microfluidic device quantitative. The colorimetric results show linearity over a wide concentration range (102CFU/mL to 108CFU/mL) and a limit of detection (LOD) of 103CFU/mL and 102CFU/mL for E. coli O157:H7 and S. Typhimurium, respectively. An insignificant change in colorimetric response for non-target bacteria indicates the aptasesnors are specific. The reported multiplexed colorimetric paper-based microfluidic devices is likely to perform well for on-site rapid screening of pathogenic bacteria in water and food products.
A novel statistical ink drop displacement (IDD) printer model for the direct binary search (DBS) halftoning algorithm is proposed. It is intended primarily for pagewide inkjet printers that exhibit ...dot displacement errors. The tabular approach in the literature predicts the gray value of a printed pixel based on the halftone pattern in some neighborhood of that pixel. However, memory retrieval time and the complexity of memory requirements hamper its feasibility in printers that have a very large number of nozzles and produce ink drops that affect a large neighborhood. To avoid this problem, our IDD model embodies dot displacements by moving each perceived ink drop in the image from its nominal location to its actual location, rather than manipulating the average gray values. This enables DBS to directly compute the appearance of the final printout without retrieving values from a table. In so doing, the memory issue is eliminated and the computation efficiency is enhanced. The deterministic cost function of DBS is replaced by the expectation over the ensemble of the displacements for the proposed model such that the statistical behavior of the ink drops is accounted for. Experimental results show significant improvement in the quality of the printed image over the original DBS. Besides, the image quality obtained by the proposed approach appears to be slightly better than that obtained by the tabular approach.
Traditional Potentiometric Ion-selective Electrodes (ISE) are widely used in industrial and clinical settings. The simplicity and small footprint of ISE have encouraged their recent adoption as ...wearable/implantable sensors for personalized healthcare and precision agriculture, creating a new set of unique challenges absent in traditional ISE. In this paper, we develop a fundamental physics-based model to describe both steady-state and transient responses of ISE relevant for wearable/implantable sensors. The model is encapsulated in a "generalized Nernst formula" that explicitly accounts for the analyte density, time-dynamics of signal transduction, ion-selective membrane thickness, and other sensor parameters. The formula is validated numerically by self-consistent modeling of multispecies ion-transport and experimentally by interpreting the time dynamics and thickness dependence of thin-film solid-contact and graphene-based ISE sensors for measuring soil nitrate concentration. These fundamental results will support the accelerated development of ISE for wearable/implantable applications.
In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are ...missing in the training data. It is challenging due to the lack of training supervision and the large geometric distortion between the freehand sketch and photo domains. To synthesize the absent freehand sketches from photos, we propose a framework that jointly learns sketch-to-photo and photo-to-sketch generation. However, the generator trained from fake sketches might lead to unsatisfying results when dealing with sketches of missing classes, due to the domain gap between synthesized sketches and real ones. To alleviate this issue, we further propose a simple yet effective open-domain sampling and optimization strategy to "fool" the generator into treating fake sketches as real ones. Our method takes advantage of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data and generalizes it to the open-domain classes. We validate our method on the Scribble and SketchyCOCO datasets. Compared with the recent competing methods, our approach shows impressive results in synthesizing realistic color, texture, and maintaining the geometric composition for various categories of open-domain sketches.
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of ...improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
The increasing incidence of infectious outbreaks from contaminated food and water supply continues imposing a global burden for food safety, creating a market demand for on-site, disposable, ...easy-to-use, and cost-efficient devices. Despite of the rapid growth of biosensors field and the generation of breakthrough technologies, more than 80% of the platforms developed at lab-scale never will get to meet the market. This work aims to provide a cost-efficient, reliable, and repeatable approach for the detection of foodborne pathogens in real samples. For the first time an optimized inkjet printing platform is proposed taking advantage of a carefully controlled nanopatterning of novel carboxyl-functionalized aptameric ink on a nitrocellulose substrate for the highly efficient detection of E. coli O157:H7 (25 colony forming units (CFU) mL
in pure culture and 233 CFU mL
in ground beef) demonstrating the ability to control the variation within ±1 SD for at least 75% of the data collected even at very low concentrations. From the best of the knowledge this work reports the lowest limit of detection of the state of the art for paper-based optical detection of E. coli O157:H7, with enough evidence (p > 0.05) to prove its high specificity at genus, species, strain, and serotype level.
The rising development of biosensors offers a great potential for health, food, and environmental monitoring. However, in many colorimetric platforms, there is a performance limitation stemming from ...the tendency of traditional Au nanoparticles toward nonspecific aggregation in response to changing ionic strength (salt concentration). This work puts forward a new type of colorimetric aptamer-functionalized labeling of microparticles, which allows to leverage an increase in ionic strength as a positive driver of enhanced detection performance of analytical targets. The resulting device is a cost-effective, instrument-free, portable, and reliable aptasensor that serves as basis for the fabrication of universal paper-based colorimetric platforms with the capability of multiplex, multireplicates and provides quantitative colorimetric detection. A controlled fabrication process was demonstrated by keeping 90% of the signal obtained from the as-fabricated devices (n = 40) within ± 1 standard deviation (SD) (relative SD = 5.69%) and following a mesokurtic normal-like distribution (p = 0.385). We propose for the first time a salt-induced aggregation mechanism for highly stable multilayered label particles (ssDNA-PEI-Au-PS) as the basis of the detection scheme. The use of DNA aptamers as capture biomolecules and PEI as an encapsulating agent allows for a sensitive and highly specific colorimetric response. As a proof of concept, multiplexed detection of mercury (Hg2+) and arsenic (As3+) was demonstrated. In addition, we introduced a robust image analysis algorithm for testing zone segmentation and color signal quantification that allowed for analytical detection, reaching a limit of detection of 1 ppm for both targeted analytes, with enough evidence (p > 0.05) to prove the high specificity of the fabricated device versus a pool of possible interferent ions.
Periodic clustered-dot screens are widely used for electrophotographic printers due to their print stability. However, moiré is a ubiquitous problem that arises in color printing due to the beating ...together of the clustered-dot, periodic halftone patterns that are used to represent different colorants. The traditional solution in the graphic arts and printing industry is to rotate identical square screens to angles that are maximally separated from each other. However, the effectiveness of this approach is limited when printing with more than four colorants, i.e., N -color printing, where N > 4 . Moreover, accurately achieving the angles that have maximum angular separation requires a very high-resolution plate writer, as is used in commercial offset printing. Commercially available high-end digital printers cannot achieve this resolution. In this paper, we propose a systematic way to design color screen sets for periodic, clustered-dot screens that offer more explicit control of the moiré properties of the resulting screens when used in color printing. We develop a principled approach for the moiré-free screen design that is called lattice-based screen design. The basic concept behind our approach is the creation of the screen set on a 2D lattice in the frequency domain, and then picking each fundamental frequency vector of the individual colorant planes in the created spectral lattice according to the desired properties. The lattice-based screen design offers more flexibility in designing N -color screen sets with different halftone geometries, and all of them are guaranteed to be all-orders moiré-free. We demonstrate the efficacy of our proposed method by introducing several new screen designs, and a comparison with published screen designs.
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