Pixel Scanning Research Articles

High-bandwidth noise-reduced loss-corrected autobalanced detection.

A compact and easy-to-use high-bandwidth autobalanced detector for microscopy is presented, being able to remove up to 67 dB of correlated noise, thus, allowing for shot-noise limited image acquisition even in the presence of high laser excess noise. Detecting a 20 MHz modulation frequency at half the repetition rate of the driving pulsed laser, the autobalanced detector is able to exploit an extra +3 dB increase in signal-to-noise ratio due to the coherent addition of modulation sidebands in stimulated Raman scattering. Pixel-by-pixel noise canceling and correction of sample transmission losses are possible for pixel scan rates of more than 1.7 Mpx/s, enabling full autobalanced operation at imaging speeds of more than 6.5 frames per second for 512 x 512 px images. It is demonstrated that the autobalanced detector is able to separate an overlaying parasitic loss-induced image from a stimulated Raman scattering microscopy image, providing both the noise and transmission corrected stimulated Raman scattering image as well as an attenuation image simultaneously.

Single-pixel positron beam diagnosis via compressive sampling.

The morphology is a crucial indicator for diagnosing a low-energy, low-brightness particle beam. However, conventional positron beam diagnosis, based on the pixel scanning principle, is limited by physical constraints, such as the resolution of detector pixels. Here, we have presented a novel slow positron diagnosis method using compressive sampling. With a 100 × 100 pixel-sized mask, for example, the positron beam morphology can be significantly reconstructed with a peak signal-to-noise ratio of ∼40 dB, even at half the sampling rate compared to pixel scanning. It explores a promising approach for positron beam diagnosis with an ultra-high resolution and fast sampling rates.

Encrypted image processing using compression and reversible data hiding

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.

Investigating microplastics and nanoplastics released from food bag ziplock using SEM and Raman imaging

Microplastic contamination is a concern in our daily lives, such as being released from self-sealing ziplock (sliderless zipper) plastic bags that are commonly used for food storage. That is because during the closure and opening process, due to friction and deformation, the male rim inserting into or separating from the female rim can release debris as micro- and nanoplastics (MNP). Herein, we initially observed the released debris using scanning electron microscopy (SEM). Subsequently, Raman imaging was employed to directly visualise the debris, either scratched on the rim surface or fallen down from the rim, from molecular spectrum perspective. Raman imaging analyses MNP from hundreds to thousands of spectra rather than from a single spectrum or peak, enhancing the signal-to-noise ratio statistically and providing morphological information for quantification. The confocal Raman-based mapping of MNP may be susceptible to be false images, which can be improved through terrain 3D mapping. Additionally, the weak signal of nanoplastics can be enhanced by reducing scanning pixel size and deconvoluting with surface-fitting algorithm. Consequently, we estimated that approximately 5(±3) MNP per millimetre along the ziplock length may be released during each closure/opening process. Given the use of these plastic bags for food storage, this level of contamination is concerning, warranting careful risk assessment alongside other potential MNP sources of plastic items used in our kitchens. Overall, Raman imaging can be effectively analyse MNP and more broadly nanomaterials, with help of algorithms and SEM.

Fast wavefront shaping for two-photon brain imaging with multipatch correction.

Nonlinear fluorescence microscopy promotes in-vivo optical imaging of cellular structure at diffraction-limited resolution deep inside scattering biological tissues. Active compensation of tissue-induced aberrations and light scattering through adaptive wavefront correction further extends the accessible depth by restoring high resolution at large depth. However, those corrections are only valid over a very limited field of view within the angular memory effect. To overcome this limitation, we introduce an acousto-optic light modulation technique for fluorescence imaging with simultaneous wavefront correction at pixel scan speed. Biaxial wavefront corrections are first learned by adaptive optimization at multiple locations in the image field. During image acquisition, the learned corrections are then switched on the fly according to the position of the excitation focus during the raster scan. The proposed microscope is applied to in vivo transcranial neuron imaging and demonstrates multi-patch correction of thinned skull-induced aberrations and scattering at 40-kHz data acquisition speed.

Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Universal Behavior of the Image Resolution for Different Scanning Trajectories

This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio NP, which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.

Raman imaging for the analysis of silicone microplastics and nanoplastics released from a kitchen sealant.

Plastic products are used ubiquitously and can potentially release microplastics and nanoplastics into the environment, for example, products such as the silicone sealant used in kitchens. It is important to develop an effective method to monitor these emerging contaminants, as reported herein. By using advanced Raman imaging to characterize microplastics and nanoplastics from hundreds of spectra in a scanning spectrum matrix and not from a single spectrum or peak, the signal-to-noise ratio can be significantly increased, from a statistical point of view. The diffraction of the laser spot usually constrains the imaging resolution (such as at ∼300nm), which is also pushed to the limit in this report by shrinking the scanning pixel size down to ∼50nm to capture and image small nanoplastics effectively. To this end, image reconstruction is developed to successfully pick up the meaningful Raman signal and intentionally avoid the noise. The results indicate that the silicone sealant in a kitchen can release a significant amount of microplastics and nanoplastics. Overall, advanced Raman imaging can be employed to characterize the microplastics and even nanoplastics that are smaller than the diffraction limit of the laser via Raman imaging and image reconstruction toward deconvolution.

Moiré fringes in conductive atomic force microscopy

Moiré physics plays an important role in characterization of functional materials and engineering of physical properties in general, ranging from strain-driven transport phenomena to superconductivity. Here, we report on the observation of moiré fringes in conductive atomic force microscopy (cAFM) scans gained on the model ferroelectric Er(Mn,Ti)O3. By performing a systematic study of the impact of key experimental parameters on the emergent moiré fringes, such as scan angle and pixel density, we demonstrate that the observed fringes arise due to a superposition of the applied raster scanning and sample-intrinsic properties, classifying the measured modulation in conductance as a scanning moiré effect. Our findings are important for the investigation of local transport phenomena in moiré engineered materials by cAFM, providing a general guideline for distinguishing extrinsic from intrinsic moiré effects. Furthermore, the experiments provide a possible pathway for enhancing the sensitivity, pushing the resolution limit of local transport measurements by probing conductance variations at the spatial resolution limit via more long-ranged moiré patterns.

Detection of microplastics and nanoplastics released from a kitchen blender using Raman imaging

Microplastics and nanoplastics have secretly entered our daily lives but the extent of the problem is still unclear, as the characterisation is still a challenge, particularly for nanoplastics. Herein we test a blender that we use in our kitchen to make juice and we find that a significant amount of microplastics and nanoplastics (∼0.36–0.78 × 109 within 30 s) are released from the plastic container. We advance the characterisation of microplastics and nanoplastics using Raman imaging to generate a scanning spectrum matrix, akin to a hyperspectral matrix, which contains 900 spectra (30 × 30). By mapping these hundreds of spectra as images, with help of algorithms, we can directly visualise the microplastics and nanoplastics with an increased sensitivity from statistical point of view. Raman imaging has a main disadvantage of the imaging resolution, originating from the diffraction of the laser spot, which is proposed to be improved by shrinking the scanning pixel size, zooming in the scanning area to capture details of nanoplastics. Using image re-construction towards deconvolution, the nanoplastics can be effective characterised and the bumpy image of microplastics stemming from the signal variation can be subsequently smoothened to further increase the signal-noise ratio. Overall, the advancements on Raman imaging can provide a suitable approach to characterise microplastics and nanoplastics released in our daily lives, for which we should be cautious.

A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.

Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images. A total of 7957 IVCM images were included for model training and testing. Scanning depth information and pixel information of IVCM images were used to build the classification system. Firstly, two base classifiers based on convolutional neural networks and K-nearest neighbors were constructed. Second, two hybrid strategies, namely weighted voting method and light gradient boosting machine (LightGBM) algorithm were used to fuse the results from the two base classifiers and obtain the final classification. Finally, the confidence of prediction results was stratified to help find out model errors. Both two hybrid systems outperformed the two base classifiers. The weighted area under the curve, weighted precision, weighted recall, and weighted F1 score were 0.9841, 0.9096, 0.9145, and 0.9111 for weighted voting hybrid system, and were 0.9794, 0.9039, 0.9055, and 0.9034 for the light gradient boosting machine stacking hybrid system, respectively. More than one-half of the misclassified samples were found using the confidence stratification method. The proposed hybrid approach could effectively integrate the scanning depth and pixel information of IVCM images, allowing for the accurate identification of corneal layers for grossly normal IVCM images. The confidence stratification approach was useful to find out misclassification of the system. The proposed hybrid approach lays important groundwork for the automatic identification of the corneal layer for IVCM images.

Event-based hyperspectral EELS: towards nanosecond temporal resolution

The acquisition of a hyperspectral image is nowadays a standard technique used in the scanning transmission electron microscope. It relates the spatial position of the electron probe to the spectral data associated with it. In the case of electron energy loss spectroscopy (EELS), frame-based hyperspectral acquisition is much slower than the achievable rastering time of the scan unit (SU), which sometimes leads to undesirable effects in the sample, such as electron irradiation damage, that goes unperceived during frame acquisition. In this work, we have developed an event-based hyperspectral EELS by using a Timepix3 application-specific integrated circuit detector with two supplementary time-to-digital (TDC) lines embedded. In such a system, electron events are characterized by their positional and temporal coordinates, but TDC events only by temporal ones. By sending reference signals from the SU to the TDC line, it is possible to reconstruct the entire spectral image with SU-limited scanning pixel dwell time and thus acquire, with no additional cost, a hyperspectral image at the same rate as that of a single channel detector, such as annular dark-field. To exemplify the possibilities behind event-based hyperspectral EELS, we have studied the decomposition of calcite (CaCO$_3$) into calcium oxide (CaO) and carbon dioxide (CO$_2$) under the electron beam irradiation.

Event-based hyperspectral EELS: towards nanosecond temporal resolution

The acquisition of a hyperspectral image is nowadays a standard technique used in the scanning transmission electron microscope. It relates the spatial position of the electron probe to the spectral data associated with it. In the case of electron energy loss spectroscopy (EELS), frame-based hyperspectral acquisition is much slower than the achievable rastering time of the scan unit (SU), which sometimes leads to undesirable effects in the sample, such as electron irradiation damage, that goes unperceived during frame acquisition. In this work, we have developed an event-based hyperspectral EELS by using a Timepix3 application-specific integrated circuit detector with two supplementary time-to-digital (TDC) lines embedded. In such a system, electron events are characterized by their positional and temporal coordinates, but TDC events only by temporal ones. By sending reference signals from the SU to the TDC line, it is possible to reconstruct the entire spectral image with SU-limited scanning pixel dwell time and thus acquire, with no additional cost, a hyperspectral image at the same rate as that of a single channel detector, such as annular dark-field. To exemplify the possibilities behind event-based hyperspectral EELS, we have studied the decomposition of calcite (CaCO3) into calcium oxide (CaO) and carbon dioxide (CO2) under the electron beam irradiation.

Chaos-Based Fast Image Encryption Scheme with Double Zigzag Permutation and Secure SHA256

Encryption is generally used to protect the content of images. This paper proposes a simple chaos-based color image encryption algorithm with permutation and diffusion stages dependent on the plain image to increase plain text sensitivity by collaboratively leveraging a sampled random noise signal, SHA-256, Henon chaotic map, double zigzag scanning and Lorenz system. Specifically, a hash value of the plain image and a sampled true random noise signal are generated using SHA-256 hash function and considered as an initial one-time key, which is then used to locate values from the sampled true random noise signal array to define the initial values and parameters for chaotic Henon map and Lorenz system. The Henon chaotic map creates an output that defines the starting point for double zigzag pixel scanning in the permutation stage, whereas the Lorenz system generates three sequences to diffuse the image’s three components; red, green, and blue using the XOR operation in order to get an encrypted image. Finally, statistical analysis results are offered to evaluate the complexity and ascertain security functionality for the proposed algorithm. Results show that the proposed image encryption algorithm is superior in comparison to other existing algorithms.

Visual navigation path extraction of orchard hard pavement based on scanning method and neural network

The accurate extraction of navigation path is very important for the automatic navigation of agricultural robots. Aiming at the complex orchard environment and the problem that the existing navigation path extraction algorithms are too complex and narrow application range, a visual navigation path extraction method based on neural network and pixel scanning was proposed in this paper. This method trained the semantic segmentation network based on Segnet and Unet on the basis of orchard road condition data sets. According to the edge of the orchard road condition mask area, the navigation path was fitted by the designed scanning method, filtering algorithm and weighted average method. The experimental results showed that the segmentation accuracy of neural network under low light, ordinary light and strong light was 96.00%, 92.00% and 92.00% respectively. The average pixel error was 9.5 pixel and the average distance error was 5.03 cm. In the actual orchard environment, the orchard road was generally 3.0 m, the average distance error accounted for 1.67%. Therefore, this method improves the accuracy of orchard visual navigation path extraction, meeting the operation requirements of tracked robots in orchard, and provides an effective reference for visual navigation task.

Reduced order model approach for imaging with waves

We introduce a novel, computationally inexpensive approach for imaging with an active array of sensors, which probe an unknown medium with a pulse and measure the resulting waves. The imaging function is based on the principle of time reversal in non-attenuating media and uses a data driven estimate of the ‘internal wave’ originating from the vicinity of the imaging point and propagating to the sensors through the unknown medium. We explain how this estimate can be obtained using a reduced order model (ROM) for the wave propagation. We analyze the imaging function, connect it to the time reversal process and describe how its resolution depends on the aperture of the array, the bandwidth of the probing pulse and the medium through which the waves propagate. We also show how the internal wave can be used for selective focusing of waves at points in the imaging region. This can be implemented experimentally and can be used for pixel scanning imaging. We assess the performance of the imaging methods with numerical simulations and compare them to the conventional reverse-time migration method and the ‘backprojection’ method introduced recently as an application of the same ROM.

A study of the influencing factors of DNAPLs transport in stochastic network fractures

The migration and distribution of dense non-aqueous phase liquid (DNAPLs) in fractured media are affected by many factors, including the physical and chemical properties of DNAPLs, geometric and chemical properties of fractures and leakage conditions. Previous studies were mostly conducted in a single fracture, while the migration of DNAPLs in stochastic network fracture were seldom examined. In this paper, stochastic network fractures are generated using the Monte Carlo method. The coordinates and apertures of fractures are identified and output by pixel scanning. The migration of Perchloroethylene (PCE) in the stochastic network fractures is simulated by PetraSim. The effects of the geometric properties of the fractures and the leakage conditions (including the leakage rate and the leakage location) on the migration of DNAPLs are discussed. The numerical simulation results show that the spatial variability of fractures also affects the migration path and spatial distribution of DNAPLs. With the increase of spatial variability of fracture width in the network fractures, dominant channels appear, the migration rate of DNAPLs accelerates, and the location of DNAPLs centroid and the spatial distribution of saturation change significantly. The leakage rate affects the migration range and spatial distribution of DNAPLs. The higher the leakage rate is, the faster the migration rate of DNAPLs will be. The more saturated the accumulated DNAPLs at the bottom of the model, the greater the spatial distribution of DNAPLs is. In the same network fracture, different leakage locations will also lead to different migration paths and distribution ranges of DNAPLs. The spatial variability of fractures in the gravity direction at different leakage locations is different, leading to different migration paths and spatial distribution of DNAPLs.

Wide-Field Pixel Super-Resolution Colour Lensfree Microscope for Digital Pathology.

Whole slide imaging enables scanning entire stained-glass slides with high resolution into digital images for the tissue morphology/molecular pathology assessment and analysis, which has increased in adoption for both clinical and research applications. As an alternative to conventional optical microscopy, lensfree holography imaging, which offers high resolution and a wide field of view (FOV) with digital focus, has been widely used in various types of biomedical imaging. However, accurate colour holographic imaging with pixel super-resolution reconstruction has remained a great challenge due to its coherent characteristic. In this work, we propose a wide-field pixel super-resolution colour lensfree microscopy by performing wavelength scanning pixel super-resolution and phase retrieval simultaneously on the three channels of red, green and blue (RGB), respectively. High-resolution RGB three-channel composite colour image is converted to the YUV space for separating the colour component and the brightness component, keeping the brightness component unchanged as well as enhancing the colour component through average filter, which not only eliminates the common rainbow artifacts of holographic colour reconstruction but also maintains the high-resolution details collected under different colour illuminations. We conducted experiments on the reconstruction of a USAF1951, stained lotus root and red bone marrow smear for performance evaluation of the spatial resolution and colour reconstruction with an imaging FOV >40 mm2.

Fast Photon-Counting Imaging With Low Acquisition Time Method

Photon-counting LIDAR has effective ability to detect significantly weak echo. However, for imaging application, it typically needs a long acquisition time to obtain sufficient counts to reconstruct clear depth image, which obviously limits imaging efficiency and prevents its wide deployment. In this paper, we proposed a low acquisition time photon-counting imaging method based on two single element SPADs and pixel scanning structure. Compared to fast imaging with SPAD array, the method has low requirement on laser energy and can obtain depth image with arbitrary pixel resolution. Experiment result demonstrates its feasibility and a dynamic target can be clearly captured by the system with our specifically designed reconstruction algorithm. We also detailly investigated the working conditions for the method. It reveals more than 2 echo photons per pulse would be enough to reconstruct clear depth image even when noise is intensive. Restricted to our 10 KHz laser source, the result only exhibited 1 frame per second(fps) imaging with pixel resolution of 100*100. If a common 10 MHz laser is adopted, 512*512 pixels resolution images can be captured at the rate of nearly 40 fps.

Crack grid detection and calculation based on convolutional neural network

Pavement crack detection has always been an important problem in pavement maintenance. With rapid development in computer vision technology, crack detection using deep learning has become one of the major tasks in automatic pavement condition detection and assessment. This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different scales (10 × 10, 20 × 20, 30 × 30), and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realizes complete crack detection. The experimental results show that 30 × 30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks. Thresholding smaller grids allows better combinations of the differences between objects and backgrounds, which makes segmentation results more accurate.