Image Splitting Research Articles

TCFormer: Visual Recognition via Token Clustering Transformer.

Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid region with a vision token. However, fixed token distribution disregards the semantic meaning of different image regions, resulting in sub-optimal performance. To address this issue, we propose the Token Clustering Transformer (TCFormer), which generates dynamic vision tokens based on semantic meaning. Our dynamic tokens possess two crucial characteristics: (1) Representing image regions with similar semantic meanings using the same vision token, even if those regions are not adjacent, and (2) concentrating on regions with valuable details and represent them using fine tokens. Through extensive experimentation across various applications, including image classification, human pose estimation, semantic segmentation, and object detection, we demonstrate the effectiveness of our TCFormer.

On-Tree Mango Fruit Count Using Live Video- Split Image Dataset to Predict Better Yield at Pre-Harvesting Stage

This study introduces a method for fruit counting in agricultural settings using video capture and the YOLOv7 object detection model. By splitting captured videos into frames and strategically selecting representative frames, the approach aims to accurately estimate fruit counts while minimizing the risk of double counting. YOLOv7, known for its efficiency and accuracy in object detection, is employed to analyze selected frames and detect fruits on trees. Demonstrated the method's effectiveness through its ability to provide farmers with precise yield estimations, optimize resource management, and facilitate early detection of orchard issues such as pest infestations or nutrient deficiencies. This technological integration reduces labor costs and supports sustainable agricultural practices by improving productivity and decision-making capabilities. The scalability of the approach makes it suitable for diverse orchard sizes and types, offering a promising tool for enhancing agricultural efficiency and profitability. The researcher compared YOLOv5n, YOLOv5s, YOLOv7, and YOLOv7-tiny with eight-sided imaging techniques around the tree. The experimental results of YOLOv7 with the eight-sided technique performed best and achieved a count accuracy of 97.7% on a single tree in just 17.112 ms of average inference time. On multiple trees, it is 95.48% in just 17 ms of average inference time, with the help of an eight- sided method on tree images.

DOCK1 deficiency drives placental trophoblast cell dysfunction by influencing inflammation and oxidative stress, hallmarks of preeclampsia.

Preeclampsia (PE) is a globally prevalent obstetric disorder, pathologically characterized by abnormal placental development. Dysfunctions of angiogenesis, vasculogenesis and spiral artery remodeling are demonstrated to be involved in PE pathogenesis; however, the underlying mechanisms remain largely unknown. Here, we investigated the role of the dedicator of cytokinesis 1 (DOCK1), crucial molecule in various cellular processes, in PE progression using HTR-8 cells derived from first-trimester placental extravillous trophoblasts. Our analysis revealed an aberrant DOCK1 expression in the placental villi of PE patients and its impact on essential cellular functions for vascular network formation. A deficiency of DOCK1 in HTR-8 cells impaired the vascular network formation, exacerbated the expression of anti-angiogenic factor ENG, and reduced VEGF levels. Moreover, DOCK1 knockout amplified apoptosis, as indicated by an altered BCL2: BAX ratio and enhanced levels of cleaved PARP. DOCK1 depletion also boosted NF-κB activation and pro-inflammatory cytokine production (IL-6 and TNF-α). Furthermore, the mice treated with DOCK1 inhibitor, TBOPP, exhibited PE-like symptoms. These findings highlight the multifaceted roles of DOCK1 in the pathophysiology of PE, demonstrating that its deficiency can lead to placental dysfunction by orchestrating inflammatory responses and oxidative stress. These insights emphasize the pathogenic role of DOCK1 in PE development and suggest potential treatment strategies that require further exploration. In the graphical abstract, a split image of placental villi contrasts the effects of normal and reduced DOCK1 expression on preeclampsia. The left side illustrates adequate DOCK1 levels supporting healthy trophoblast function and effective spiral artery remodeling. The right side highlights the consequences of DOCK1 deficiency, leading to trophoblast dysfunction and impaired spiral artery remodeling, accompanied by angiogenic imbalance, increased inflammation, oxidative stress, and apoptosis, contributing to placental dysfunction and the development of preeclampsia.

Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks

As facial modification technology advances rapidly, it poses a challenge to methods used to detect fake faces. The advent of deep learning and AI-based technologies has led to the creation of counterfeit photographs that are more difficult to discern apart from real ones. Existing Deep fake detection systems excel at spotting fake content with low visual quality and are easily recognized by visual artifacts. The study employed a unique active forensic strategy Compact Ensemble-based discriminators architecture using Deep Conditional Generative Adversarial Networks (CED-DCGAN), for identifying real-time deep fakes in video conferencing. DCGAN focuses on video-deep fake detection on features since technologies for creating convincing fakes are improving rapidly. As a first step towards recognizing DCGAN-generated images, split real-time video images into frames containing essential elements and then use that bandwidth to train an ensemble-based discriminator as a classifier. Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. Python tool is used for implementing this proposed study and the accuracy obtained for proposed work is 98.23 %.

Novel physiological analysis using blood flow velocity for colonic polyps: Pilot study.

Real-time visualization of red blood cell flow inside subepithelial microvessels is performed with magnifying endoscopy. However, microvascular blood flow velocity in the colorectum has not been investigated. Here, we aimed to evaluate the blood flow velocity of microvessels of colonic polyps and to compare it with that of surrounding mucosa. We examined 50 lesions, including 30 adenomas (ADs) and 20 hyperplastic polyps (HPs). Blood flow velocities of lesions and their surrounding mucosa were evaluated using magnifying blue laser imaging (BLI) prior to endoscopic resection. Calculation of mean blood flow velocities was based on mean movement distance of one tagged red blood cell using split video images of magnifying BLI. Mean microvascular blood flow velocity was significantly lower in ADs (1.65±0.66 mm/sec; range 0.46-2.90) than in HPs (2.83±1.10 mm/sec; 1.07-4.50) or the surrounding mucosa (3.73±1.11 mm/sec; 1.80-6.20; P <0.001). The blood flow velocity rate compared with the surrounding mucosa was significantly lower in ADs (0.41±0.16; 0.10-0.82) than in HPs (0.89±0.25; 0.46-1.51; P <0.001). We found that mean microvascular blood flow velocity was significantly lower in ADs than in HPs and the surrounding non-neoplastic mucosa. These findings indicate that a novel dynamic approach with microvascular blood flow velocity using magnifying endoscopy may be useful in assessing physiological differences between ADs and HPs.

Towards concentric spatial systems for sustainable social development: Beyond western ethnocentric diametric spatial opposition and empty space

&lt;p&gt;This article outlines key features of an emerging spatial turn in education, the social sciences and humanities and its relevance to developing sustainable social systems, with a particular focus on inclusive systems. This is cognisant of UN Sustainable Goal 4 on Equitable Inclusive Education and Goal 1 on No Poverty. Offering a necessarily illustrative selection of key conceptual traditions and recent applications of spatial understandings pertinent to education and inclusion, with wider applicability, this proposed spatial turn is examined as offering critical alternatives to Western ethnocentric frames of space. This leads to contrasts between concentric spatial systems of inclusion, assumed connection and relative openness and diametric spatial systems of exclusion, splitting and mirror image oppositions in education and community spaces of relation.&lt;/p&gt;

Deep learning autism classification and prediction

One of the most prevalent illnesses in children is autism spectrum disorder (ASD) (1 in 44). According to some estimates, 53% of kids with ASD engage in one or more challenging behaviors (CB; aggression, self-injury, property destruction, elopement, etc.), which is significantly higher than the prevalence among their peers who are typically developing or who have other developmental disorders. Numerous, significant negative effects of CB on the person exist, and they are linked to a worse long-term outlook. For caregivers of children with ASD, the presence of CB is a better indicator of stress than the severity of the child's core ASD symptoms. The validity of fixed features extracted from autistic children’s face photographs as a biomarker to demarcate them from healthy children is investigated in this study paper. The proposed paper aims to use deep learning models (CNN) to classify autism spectrum disorders based on facial expression images. By leveraging the power of deep convolutional neural networks, based on the Kaggle dataset. We used and prepared data input to CNN models where the split image in two parts horizontally and vertically as feature extractor’s model as a binary classifier to identify autism in children accurately. Our results reveal that the proposed model achieved an accuracy of 94%, Sensitivity of 93% and Specificity of 95% this indicator is considered important and can be built or relied on.

Cataract classification and grading in federated learning environment using CNN based Modified-ResNet-50

Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent.

Crosstalk between KDEL receptor and EGF receptor mediates cell proliferation and migration via STAT3 signaling

Hostile microenvironment of cancer cells provoke a stressful condition for endoplasmic reticulum (ER) and stimulate the expression and secretion of ER chaperones, leading to tumorigenic effects. However, the molecular mechanism underlying these effects is largely unknown. In this study, we reveal that the last four residues of ER chaperones, which are recognized by KDEL receptor (KDELR), is required for cell proliferation and migration induced by secreted chaperones. By combining proximity-based mass spectrometry analysis, split venus imaging and membrane yeast two hybrid assay, we present that EGF receptor (EGFR) may be a co-receptor for KDELR on the surface. Prior to ligand addition, KDELR spontaneously oligomerizes and constantly undergoes recycling near the plasma membrane. Upon KDEL ligand binding, the interactions of KDELR with itself and with EGFR increase rapidly, leading to augmented internalization of KDELR and tyrosine phosphorylation in the C-terminus of EGFR. STAT3, which binds the phosphorylated tyrosine motif on EGFR, is subsequently activated by EGFR and mediates cell growth and migration. Taken together, our results suggest that KDELR serves as a bona fide cell surface receptor for secreted ER chaperones and transactivates EGFR-STAT3 signaling pathway.

Integrated Diffusion Tensor Imaging and Renal Parenchymal Volume for Early Detection and Grading of Split Renal Functional Impairment in Lupus Nephritis

To investigate the effectiveness of combining split diffusion tensor imaging (DTI) measurements with split renal parenchymal volume (RPV) for assessing split renal functional impairment in patients with lupus nephritis (LN). Seventy-four participants [48 LN patients and 26 healthy volunteers (HV)] were included in the study. All participant underwent conventional MR and DTI (b = 0, 400, and 600s/mm2) examinations using a 3.0T MRI scanner to determine the split renal DTI measurements and split RPV. In LN patients, renography glomerular filtration rate (rGFR) was measured using 99mTc-DTPA scintigraphy based on Gates' method, serving as the reference standard to categorize all split kidneys of LN patients into LN with mild impairment (LNm, n=65 kidneys) and LN with moderate to severe (LNms, n=31 kidneys) groups according to the threshold of 30ml/min in spilt rGFR. All statistical analyses were performed using SPSS 25.0 and MedCalc 20.0 software packages. Only split medullary fractional anisotropy (FA) and the product of split medullary FA and RPV could distinguish pairwise subgroups among the HV and each LN subgroup (all p<0.05). ROC curve analysis demonstrated that split medullary FA (AUC=0.866) significantly outperformed other parameters in differentiating HV from LNm groups, while the product of split medullary FA and split RPV was superior in distinguishing LNm and LNms groups (AUC=0.793) than other parameters. The combination of split medullary FA and split RPV showed best correlation with split rGFR (r=0.534, p<0.001). Split medullary FA, and its combination with split RPV, are valuable biomarkers for detecting early functional changes in renal alterations and predicting disease progression in patients with LN.

A multi-species pest recognition and counting method based on a density map in the greenhouse

Whiteflies and fruit flies are common pests that adversely affect greenhouse crops, so it is vital to control their numbers promptly. Current research involves setting up artificial monitoring devices with fixed yellow sticky traps (YSTs), for pest surveillance. However, these devices can only be installed in specific environments and additional interventions may impact the actual distribution of greenhouse pests. On the other hand, detection methods, which are the most commonly used, struggle to accurately detect overlapping or closely interacting small insects, which is a common occurrence in the presence of pests. In this paper, we propose a novel solution for the multi-species pest recognition and counting method based on a density map to tackle pest counting in the greenhouse. To reduce interference between different types of objects, we used two annotation maps to represent the distribution of each object separately. Then, two counting branches are designed to count the pests, respectively. Finally, the detected pests are mapped onto the original image to display their spatial positions. In addition, an adaptive sample equalization method is proposed to address the issue of class imbalance in images, and the atrous spatial pyramid pooling (ASPP) structure and the CBAM attention mechanism are added to the fruit fly branches to focus on learning fruit fly features from crowded edges of YSTs. When applied to split images, our method achieved reliable counting performance in counting both whiteflies and fruit flies, with R2 scores of 0.973 and 0.972, respectively. This method has been embedded into mobile devices to assist greenhouse workers in monitoring pest populations in the field. The experiments show that the proposed method provides a promising direction for counting multiple small target insects and monitoring pest populations in crowded images. At the same time, it demonstrates the potential of density map methods in various small target detection scenarios.

Effects of visual stimuli on auditory separation of sound images spatially split by synthesized binaural signal

In order to clarify the effect of visual stimuli on the spatially split perception of sound images by interaural differences, we conducted spatially split perception experiments under conditions in which visual stimuli were presented on a head mounted display. The auditory stimuli were synthesized binaural signals consisting of two uncorrelated pink noises convolved with the receiver's own head related impulse responses. As manipulated by the listener using the mouse scroll-wheel, the visual and auditory stimuli spread to the left and right in front of the listener, and the listener was made to answer at what point the sound images were spatially split into two. As a result, the detection limit was smaller in the conditions with split visual stimuli than in the condition without visual stimulus. These results suggest that visual stimuli that give the impression of separation reduce the detection limit of the auditorily perceived spatially split sound images.

A vision transformer for fine-grained classification by reducing noise and enhancing discriminative information

Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks. However, there are some limitations when applying ViT directly to FGVC. First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy noise calculation during the training phase and unsatisfying performance when handling fine-grained images with complex backgrounds and small objects. Second, complementary information is important for FGVC, but a standard ViT works by using the class token in the final layer for classification which is not enough to extract comprehensive fine-grained information at different levels. Third, the class token fuses the information of all patches in the same manner, in other words, the class token treats each patch equally. However, the discriminative parts should be more critical. To address these issues, we propose ACC-ViT including three novel components, i.e., Attention Patch Combination (APC), Critical Regions Filter (CRF), and Complementary Tokens Integration (CTI). Thereinto, APC pieces informative patches from two images to generate a new image to mitigate the noisy calculation and reinforce the differences between images. CRF emphasizes tokens corresponding to discriminative regions to generate a new class token for subtle feature learning. To extract comprehensive information, CTI integrates complementary information captured by class tokens in different ViT layers. We conduct comprehensive experiments on four widely used datasets and the results demonstrate that ACC-ViT can achieve competitive performance. The source code is available at https://github.com/Hector0426/fine-grained-image-classification-with-vit.

Validation and enhancement of a vocal fold medial surface 3D reconstruction approach for in-vivo application

In laryngeal research, studying the vertical vocal fold oscillation component is often disregarded. However, vocal fold oscillation by its nature is a three-dimensional process. In the past, we have developed an in-vivo experimental protocol to reconstruct the full, three-dimensional vocal fold vibration. The goal of this study is to validate this 3D reconstruction method. We present an in-vivo canine hemilarynx setup using high-speed video recording and a right-angle prism for 3D reconstruction of vocal fold medial surface vibrations. The 3D surface is reconstructed from the split image provided by the prism. For validation, reconstruction error was calculated for objects located at a distance of up to 15 mm away from the prism. The influence of camera angle, changing calibrated volume, and calibration errors were determined. Overall average 3D reconstruction error is low and does not exceed 0.12 mm at 5 mm distance from the prism. Influence of a moderate (5°) and large (10°) deviation in camera angle led to a slight increase in error to 0.16 mm and 0.17 mm, respectively. This procedure is robust towards changes in calibration volume and small calibration errors. This makes this 3D reconstruction approach a useful tool for the reconstruction of accessible and moving tissue surfaces.

Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model

This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7) to address the low accuracy of apple fruit target recognition caused by high fruit density, occlusion, and overlapping issues. Firstly, we proposed a preprocessing algorithm for the split image with overlapping to improve the robotic intelligent picking recognition accuracy. Then, we divided the training, validation, and test sets. Secondly, the MobileOne module was introduced into the backbone network of YOLOv7 to achieve parametric fusion and reduce network computation. Afterward, we improved the SPPCSPS module and changed the serial channel to the parallel channel to enhance the speed of image feature fusion. We added an auxiliary detection head to the head structure. Finally, we conducted fruit target recognition based on model validation and tests. The results showed that the accuracy of the improved YOLOv7 algorithm increased by 6.9%. The recall rate increased by 10%, the mAP1 algorithm increased by 5%, and the mAP2 algorithm increased by 3.8%. The accuracy of the improved YOLOv7 algorithm was 3.5%, 14%, 9.1%, and 6.5% higher than that of other control YOLO algorithms, verifying that the improved YOLOv7 algorithm could significantly improve the fruit target recognition in high-density fruits.

Learning to high-performance autofocus microscopy with laser illumination

The autofocusing plays a crucial role in various industrial microscopic measurements. However, conventional autofocus methods involve axial scanning and are prone to inaccuracies caused by surface noises, such as defects and texture, making them both time-consuming and unreliable. In this work, a novel high-performance autofocus pipeline with laser illumination was introduced, namely Learning Split-image Prediction Model (LSPM), to accurately predict the defocus distance from a single split image. To effectively reduce the interference of the noises as foregrounds, a lightweight generation network was built to restore the incomplete split images. Furthermore, a new judgment mechanism for split images was proposed to identify the ideal focal plane of each focal stack. The experiment demonstrated LSPM outperforms the state-of-the-art methods, in terms of focusing accuracy, especially when the defocusing amount is relatively small, achieving only 1/15 depth of field. LSPM has the advantages of high accuracy, high speed, and robustness in industrial measurement.

V01-03 SINGLE-PORT MINI PFANNENSTIEL ROBOTIC EXTRAPERITONEAL PEDIATRIC PYELOPLASTY: INITIAL EXPERIENCE

You have accessJournal of UrologyCME1 Apr 2023V01-03 SINGLE-PORT MINI PFANNENSTIEL ROBOTIC EXTRAPERITONEAL PEDIATRIC PYELOPLASTY: INITIAL EXPERIENCE Jaya Sai Chavali, Brendan Frainey, Albert Geskin, Ethan Ferguson, Roxana Ramos, Audrey Rhee, and Jihad Kaouk Jaya Sai ChavaliJaya Sai Chavali More articles by this author , Brendan FraineyBrendan Frainey More articles by this author , Albert GeskinAlbert Geskin More articles by this author , Ethan FergusonEthan Ferguson More articles by this author , Roxana RamosRoxana Ramos More articles by this author , Audrey RheeAudrey Rhee More articles by this author , and Jihad KaoukJihad Kaouk More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003221.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We aim to report our technique and functional and radiological outcomes of extra peritoneal robotic pediatric pyeloplasty through a mini-pfannenstiel (3 cm) incision using da-Vinci single-port (SP) robotic surgical system in pediatric population. METHODS: We present our initial series of 8 pediatric patients that underwent robotic SP pyeloplasty between 2020 - 2022. The case presented in current video is 1 year old male with known history antenatal hydronephrosis that presented with worsening postnatal hydronephrosis and renal function on split function and renal ultrasound imaging studies. The patient underwent cystoscopy, stent placement that confirmed severe ureteropelvic junction (UPJ) obstruction prior to proceeding with robotic surgery. He was placed in relaxed lateral position, extra peritoneal space was developed through mini pfannenstiel incision before docking the SP surgical system. The following robotic surgical steps were described: (1) development of extra peritoneal space, (2) identification of ureter and ureteropelvic junction, (3) dismemberment and reduction of redundant renal pelvis, (4) ureteropelvic junction anastomosis using running 5-0 Vicryl sutures. RESULTS: Overall operative time was 120 minutes and associated with minimal blood loss. No complications and patient discharged on POD1 and stent removed in 4 weeks as outpatient. The attached tables describe our study patient demographics, indications (Table 1) and outcomes among our study population (Table 2). All patients tolerated the surgery with minimal postsurgical pain and no narcotic requirement. CONCLUSIONS: Single port extra peritoneal pyeloplasty is a safe and feasible option for upper tract reconstruction in pediatric patients. All patients had complete resolution of symptoms both clinically and based on imaging upon follow up. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e79 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jaya Sai Chavali More articles by this author Brendan Frainey More articles by this author Albert Geskin More articles by this author Ethan Ferguson More articles by this author Roxana Ramos More articles by this author Audrey Rhee More articles by this author Jihad Kaouk More articles by this author Expand All Advertisement PDF downloadLoading ...

Feature Sampling based on Multilayer Perceptive Neural Network for image quality assessment

Image quality assessment (IQA) has a vital issue in image processing to measure the perceptual quality of the image. This aims at the human visual system (HVS) by viewing for distorted images from reference images. HVS is the ultimate receiver of visual data in major applications. But, quality assessment is generally a time-consuming and luxurious method. In our work, the novel Bilateral Smoothing Filtered Feature Sampling-Based Tanimoto Index’ve Multilayer Perceptive Neural Network (BSFFS-TIMPNN) technique is developed. The main contribution of the designed BSFFS-TIMPNN technique is for enhancing the quality of the assessment performance with full reference and no reference images with multiple layers. At first, the original and reference image is taken to the input layer and sent to the first hidden layer. Followed by, image preprocessing is performed in the first hidden layer by using the novelty of a nonlinear bilateral smoothing filtering technique for eradicating noisy pixels with a higher-quality image. Next, patch extraction is executed in the second hidden layer with the novelty of the nearest neighbor sampling approach to split images into various patches. Then, the different features of the image such as shape, color, and texture are extracted in the third hidden layer. Finally, classification is carried out output layer by applying the innovation of the Tanimoto similarity index to examine the extracted features for IQA. We estimate the proposed BSFFS-TIMPNN technique on the Tampere Image Database (TID2013) dataset with qualitative and quantitative results analysis. The proposed BSFFS-TIMPNN technique is employed to compute image quality with maximum accuracy and minimum time and memory consumption when compared to other related methods by MATLAB simulator. The observed quantitative result demonstrates the better performance of the proposed BSFFS-TIMPNN technique by full reference and no reference with higher PSNR by 8%, accuracy by 15%, 17%, lesser memory consumption by 15%, 17, and faster prediction time by 9%, 9% than the state of art methods respectively.

Better than a lens? Increasing the signal-to-noise ratio through pupil splitting

Incoherent imaging via an unmodified full pupil seemingly yields the maximum achievable signal-to-noise ratio (SNR) with respect to a fixed photon budget. Such photon-limited SNR is critical in many imaging scenarios, for example, in the case of fluorescence microscopy. In this work, we propose a general method that achieves a better SNR for transmitting high spatial frequency information through an optical imaging system, without the need to capture more photons. This is achieved by splitting the pupil of an incoherent imaging system such that two sub-images are simultaneously acquired and computationally recombined. We compared the theoretical performance of split pupil imaging to the non-split scenario and implement the splitting using a tilted elliptical mirror covering ≈50% of the pupil, placed at the back-focal-plane (BFP) of a fluorescence widefield microscope. Additionally, the proposed system exhibits an extended-depth-of-field (EDoF), utilized further to assign some of the measured (in-focus) signal to different axial planes of the reconstructed sample, through thick slice deconvolution. Our proposed method can be modified to tailor SNR enhancements to specific metrology tasks or to exploit other properties (e.g., spectral or polarization information) for SNR enhancement with impact on future imaging schemes.

Sensitivity to naturalistic texture relies primarily on high spatial frequencies.

Natural images contain information at multiple spatial scales. Though we understand how early visual mechanisms split multiscale images into distinct spatial frequency channels, we do not know how the outputs of these channels are processed further by mid-level visual mechanisms. We have recently developed a texture discrimination task that uses synthetic, multi-scale, "naturalistic" textures to isolate these mid-level mechanisms. Here, we use three experimental manipulations (image blur, image rescaling, and eccentric viewing) to show that perceptual sensitivity to naturalistic structure is strongly dependent on features at high object spatial frequencies (measured in cycles/image). As a result, sensitivity depends on a texture acuity limit, a property of the visual system that sets the highest retinal spatial frequency (measured in cycles/degree) at which observers can detect naturalistic features. Analysis of the texture images using a model observer analysis shows that naturalistic image features at high object spatial frequencies carry more task-relevant information than those at low object spatial frequencies. That is, the dependence of sensitivity on high object spatial frequencies is a property of the texture images, rather than a property of the visual system. Accordingly, we find human observers' ability to extract naturalistic information (their efficiency) is similar for all object spatial frequencies. We conclude that the mid-level mechanisms that underlie perceptual sensitivity effectively extract information from all image features below the texture acuity limit, regardless of their retinal and object spatial frequency.