Smartphone Photography Research Articles

Cost-effective identification of the field maturity of tobacco leaves based on deep semi-supervised active learning and smartphone photograph

As a key technology for tobacco leaf harvesting, automatic identification of the field maturity of tobacco leaves is an important issue in the tobacco industry. For years, researchers have been exploring machine learning methods to perform the identification task, with traditional supervised learning (SL) schemes being studied in depth. However, the actual use of an SL algorithm is always costly and cumbersome, as it requires a large amount of labor and time to collect and label enough tobacco leaf samples. Therefore, a cost-effective algorithm solution is desired by the industry. To this end, we investigate the efficacy and efficiency of semi-supervised learning (SSL) scheme for this task, attempting to significantly reduce the labeling cost and provide a flexible way to control the cost. Considering the advantages of smartphones in terms of availability and economy, we use smartphone photography as an image acquisition tool, thereby building a tobacco leaf maturity dataset containing more than 7000 tobacco leaf images of three field maturity categories (unripe, ripe and overripe) of three stem positions (bottom, middle and top). According to the fact that the maturity of the unripe, ripe and overripe leaves increases sequentially, we propose a maturity structure constraint (MSC) to make the identification model converge to a confident space and be generalizable. Furthermore, we propose a sample selection method based on self-supervised representation learning, which can effectively select high-quality samples for the identification network. Finally, we design an SSL algorithm framework called maturity structure constraint based semi-supervised learning (MSC-SSL) for the identification task. Experimental results show that our SSL framework is capable to significantly reduce the sample usage and improve the identification accuracy. It achieves the accuracies of 91.92% (bottom position/with 33.16% training samples), 87.06% (middle position/with 27.57% training samples) and 87.64% (top position/with 28.02% training samples) when auto stopped, as compared to the SSL benchmarks of 87.37% and 90.40%, 83.53% and 85.10%, 81.65% and 84.64%, respectively. Furthermore, our method can match or exceed the accuracy of the SL scheme using only about 25% of the labeled samples required by the SL scheme.

Smartphone Photography to Communicate Science

This article discusses the important role that photographs play in science communication. Tips are given for enhancing photography using smartphones.

Diagnostic Accuracy of Smartphone Corneal Photography for Detection of Corneal Opacities in a Resource-Limited Setting: A Community-Based Study

Purpose: To evaluate the diagnostic accuracy of smartphone corneal photography in detecting corneal opacities in a community-based setting. Methods: A case-control, diagnostic accuracy study was nested in a cluster-randomized trial of a corneal ulcer prevention intervention in Nepal. Smartphone corneal photography was performed annually on community members self-reporting a potential risk factor for a corneal infection. Corneal photographs were graded for the presence or absence of an opacity. All cases with an opacity on smartphone photography and an equal number of controls were invited for a comprehensive eye examination with a slit lamp biomicroscope at an eye hospital. A mobile team visited participants unable to come to the hospital, conducting a limited examination with a penlight. Results: Of 1332 study participants (666 cases and 666 controls), 1097 had a penlight examination (535 cases and 562 controls) and 191 had a slit lamp examination (120 cases and 71 controls). When penlight examination was considered the reference standard, smartphone diagnosis of a corneal opacity had a positive predictive value (PPV) of 47% (95% confidence interval 43%–52%) and negative predictive value (NPV) of 95% (93%–97%). When slit lamp examination was considered the reference standard, the overall PPV and NPV were 71% (62%–78%) and 80% (70%–88%), respectively. The NPV was greater for detection of opacities >1 mm, estimated at 95% (90%–98%). Conclusions: Corneal photography performed in a resource-limited community-based setting using a smartphone coupled to an external attachment had acceptable diagnostic accuracy for detection of corneal opacities large enough to be clinically meaningful.

Telerehabilitation Technology Used for Remote Wrist/Finger Range of Motion Evaluation: A Scoping Review.

Monitoring finger/wrist range of motion (ROM) is an important component of routine hand therapy after surgery. Telerehabilitation is a field that may potentially address various barriers of in-person hand therapy appointments. Therefore, the purpose of this scoping review is to identify telerehabilitation technologies that can be feasibly used in a patient's home to objectively measure finger/wrist ROM. Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases using alternative word spellings for the following core concepts: "wrist/hand," "rehabilitation," and "telemedicine." Studies were imported into Covidence, and systematic two-level screening was done by two independent reviewers. Patient demographics and telerehabilitation information were extracted from the selected articles, and a narrative synthesis of the findings was done. There were 28 studies included in this review, of which the telerehabilitation strategies included smartphone angle measurement applications, smartphone photography, videoconference, and wearable or external sensors. Most studies measured wrist ROM with the most accurate technologies being wearable and external sensors. For finger ROM, the smartphone angle application and photography had higher accuracy than sensor systems. The telerehabilitation strategies that had the highest level of usability in a remote setting were smartphone photographs and estimation during virtual appointments. Telerehabilitation can be used as a reliable substitute to in-person goniometer measurements, particularly the smartphone photography and motion sensor ROM measurement technologies. Future research should investigate how to improve the accuracy of motion sensor applications that are available on easy-to-access devices.

Measuring Perceptual Color Differences of Smartphone Photographs

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop®, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.

Smartphone photos as a non-destructive approach to characterise siliceous artefacts

ABSTRACTA methodological proposal was developed for the study of prehistoric siliceous artefacts which includes the use of smartphone photography as an analytical tool. Different kinds of materials (cherts, rock crystal and quartzite) were sampled from the lithic assemblage of La Calvera rock-shelter (Camaleño, Spain), chosen as a case study. Each sample was photographed with a smartphone and colour features were compared with portable and non-destructive standardised techniques including visible spectrophotometry, Raman, diffuse reflectance Fourier transform infrared spectroscopy and portable X-ray fluorescence. The obtained results suggest that smartphone image analysis is a reliable approach to characterise siliceous rocks and that it can be a valuable, cheap and fast method especially suitable for the first screening of large lithic assemblages.

Smartphone Photography as a Teledentistry Method to Evaluate Anterior Composite Restorations.

Today, teledentistry have gain more attention than past due to the advances in technology. The aim of this study was to compare the use of smartphone photography as a method in teledentistry with the face-to-face examination in the evaluation of anterior composite restorations. In this study, photographs of 24 composite restorations in patients attended to the clinic of Ardabil Dental School were obtained using a smartphone without any additional equipment and sent by email to 10 remote observers. As a gold standard method, these restorations were evaluated by an experienced expert in restorative dentistry a face-to-face examination. In both methods FDI criteria were used to evaluate the restorations and classified them as acceptable or not. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of the photographic method relative to face-to-face method were calculated. Furthermore the Mann-Whitney U test was used to statistically compare the two methods in detail. The sensitivity, specificity, positive predictive value, and negative predictive value of the smartphone photography method was 69.35%, 48.72%, 87.34%, and 23.75%, respectively. A diagnostic accuracy of 65.97% was obtained. Statistically, in overall the photographic method rated the restorations as better than they were in reality (face-to-face examination) (p = 0.002). The diagnostic accuracy of the method of evaluating anterior composite restorations by smartphone photography was moderate and the use of this method in teledentistry, although was promising, needs to be improved.

A study establishing sensitivity and accuracy of smartphone photography in ophthalmologic community outreach programs: Review of a smart eye camera.

Diseases affecting the cornea are a major cause of corneal blindness globally. The pressing issue we are facing today is the lack of diagnostic devices in rural areas to diagnose these conditions. The aim of the study is to establish sensitivity and accuracy of smartphone photography using a smart eye camera (SEC) in ophthalmologic community outreach programs. In this pilot study, a prospective non-randomized comparative analysis of inter-observer variability of anterior segment imaging recorded using an SEC was performed. Consecutive 100 patients with corneal pathologies, who visited the cornea specialty outpatient clinic, were enrolled. They were examined with a conventional non-portable slit lamp by a cornea consultant, and the diagnoses were recorded. This was compared with the diagnoses made by two other consultants based on SEC videos of the anterior segment of the same 100 patients. The accuracy of SEC was accessed using sensitivity, specificity, PPV, and NPV. Kappa statistics was used to find the agreement between two consultants by using STATA 17.0 (Texas, USA). There was agreement between the two consultants to diagnosing by using SEC. Above 90% agreements were found in all the diagnoses, which were statistically significant (P-value < 0.001). More than 90% sensitivity and a negative predictive value were found. SEC can be used successfully in the community outreach programs like field visits, eye camps, teleophthalmology, and community centers, where either a clinical setup is lacking or ophthalmologists are not available.

MP26-03 MECHANIZED TRIAGE: PILOTING MACHINE LEARNING TO RISK STRATIFY PENILE LESIONS

You have accessJournal of UrologyCME1 Apr 2023MP26-03 MECHANIZED TRIAGE: PILOTING MACHINE LEARNING TO RISK STRATIFY PENILE LESIONS Jonathan O'Brien, Kishor Nandakishor, Kenneth Chen, Jiasian Teh, Brian Kelly, Todd Manning, Niranjan Sathianathen, Declan Murphy, Damien Bolton, Justin Chee, Marimuthu Palaniswami, and Nathan Lawrentschuk Jonathan O'BrienJonathan O'Brien More articles by this author , Kishor NandakishorKishor Nandakishor More articles by this author , Kenneth ChenKenneth Chen More articles by this author , Jiasian TehJiasian Teh More articles by this author , Brian KellyBrian Kelly More articles by this author , Todd ManningTodd Manning More articles by this author , Niranjan SathianathenNiranjan Sathianathen More articles by this author , Declan MurphyDeclan Murphy More articles by this author , Damien BoltonDamien Bolton More articles by this author , Justin CheeJustin Chee More articles by this author , Marimuthu PalaniswamiMarimuthu Palaniswami More articles by this author , and Nathan LawrentschukNathan Lawrentschuk More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003254.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Penile squamous cell carcinoma (PSCC) is a rare disease with devastating psychosocial consequences. Early recognition and treatment are paramount to preserving function and long-term survival. However, many men present with advanced disease due to a lack of awareness, social stigma, and limited access to culturally appropriate care. There is an urgent need to reduce barriers to subspecialist penile cancer care, especially for men from low socioeconomic backgrounds. Machine learning combined with smartphone photography has demonstrated growing utility by outperforming clinicians in diagnosing skin cancer. This project aims to evaluate the accuracy of an artificial intelligence algorithm for stratifying penile lesions. METHODS: A Google image search was performed for high-quality colour images of penile lesions in peer-reviewed English language articles with a formal diagnosis discussed within the article. The search terms used were “penile cancer”, “penile squamous cell carcinoma (SCC)”, “penile lesion”, “benign penile lesion”, “penile carcinoma in situ (CIS)”, “penile neoplasm in situ (PeIN)”. Images that fit inclusion criteria were downloaded as JPEG files and categorized as benign, pre-malignant, and penile SCC. Two penile cancer subspecialist urologists independently reviewed all images to confirm categorization. A deep learning algorithm was created to extract and automatically segment images into pixels. Contours of pixel edges were used to generate a “mask” representation of the lesion from normal skin. Features such as lesion elevation, erythema, ulceration, redness, and irregularity were extracted and compared. A hold-out validation methodology was performed for training and internally assessing accuracy. RESULTS: One hundred thirty-eight images from 83 articles were included – 67 invasive PSCC, 44 carcinoma in situ (CIS), and 27 benign. Subspecialist urologist agreement on image categorization was 96%. Ten rounds of algorithm training were performed on 98 randomly assigned images, equating to 980 experiments. A total of 40 images were randomly assigned to the test subset, sequestered from the algorithm, and used for internal accuracy validation. The algorithm demonstrated an overall triage accuracy of 87.5%. CONCLUSIONS: We present the first study in English literature to identify the role of artificial intelligence in accurately categorizing penile lesions. Penile lesions beneath non-retractile foreskin were identified as a limitation for image-based PSCC detection. Further image incorporation and validation with clinical images from electronic medical records are underway. An opportunity exists to refine artificial intelligence as an education, triage, and referral optimization tool via a smartphone application. In the future, artificial intelligence may help to lower barriers to accessing sub-specialist penile cancer care globally. Source of Funding: Self funded © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e352 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jonathan O'Brien More articles by this author Kishor Nandakishor More articles by this author Kenneth Chen More articles by this author Jiasian Teh More articles by this author Brian Kelly More articles by this author Todd Manning More articles by this author Niranjan Sathianathen More articles by this author Declan Murphy More articles by this author Damien Bolton More articles by this author Justin Chee More articles by this author Marimuthu Palaniswami More articles by this author Nathan Lawrentschuk More articles by this author Expand All Advertisement PDF downloadLoading ...

Spectral-Loc

For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way into the latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room, and a large office space, demonstrate that the use of light spectral information significantly reduces the localization error for the different percentiles.

An accurate determination of the refractive indices of water and glass by smartphone photography

A smartphone can be used for many physics experiments by using the sensors built into the phone. The complementary metal oxide semiconductor (CMOS) (charge-coupled device, CCD) sensor of the camera records the images of objects photographed in pixels. The width of the image can be read off with micron level accuracy with the help of software available freely from the internet. If the dimensions of the object in the direction transverse to the viewing direction of the camera are known, the magnification of the object photographed can be determined accurately. A smartphone camera is a compound lens comprising a number of very thin lenses. A single thin lens is equivalent, as far as the transverse magnification is concerned, to a compound lens of the smartphone, when the thin lens has the same focal length as the effective focal length of the compound lens and is placed at its first principal plane. This property allows us to find experimentally the effective focal length of the compound lens of the camera accurately, using the thin lens equation. Once the focal length of the camera is known, the distance of an object can be determined by finding its magnification from the photograph. We have determined the refractive index of water accurately by photographing the apparent position of an object immersed in it. By determining the magnifications, we have found the real and apparent depths of the object. The ratio of these depths gives the refractive index. We have determined the refractive index of glass also using some thin glass slides.

Evaluation of Mobile and Digital Single-Lens Reflex Photography for Facial Surgical Analysis.

High-quality perioperative photography is imperative to good surgical planning in facial reconstructive and aesthetic surgery. We explore the utility of an add-on smartphone telephoto lens to avoid the distortions noted in prior studies using smartphone cameras. Standard perioperative photographs of the same subject were taken with three distinct cameras using a dual-ring light setup. The three camera setups iPhone 11 alone, iPhone 11 with the 58 Moment telephoto lens attachment, and a D3300 Nikon DSLR APS-C sensor camera with a 60 mm NIKKOR F2.8G ED macro lens were compared using a 47-question online survey consisting of demographic and image-specific questions sent to plastic surgeons. Forty-nine facial plastic surgeons completed the survey. The iPhone 11 alone was identified as having the lowest quality for central/peripheral distortion (83%), columella/caudal septum/alar anatomy (58.3%), and skin quality (38.3%). With the addition of the telephoto lens, the ability to assess all categories was significantly improved. 53.1% (n= 26) of respondents found the iPhone 11 + 58 mm telephoto lens setup to be the most useful for perioperative surgical planning. Smartphone photography with the addition of a telephoto lens can offer a comparable option to the DSLR with regard to photo quality and detail. A telephoto add-on lens is an effective solution to overcome the central distortion seen in images taken by the iPhone for perioperative photography. This photo quality was found to be comparable to that of traditional DSLR cameras in our survey study. 3 Laryngoscope, 133:2590-2596, 2023.

Smartphone Photography: Empowerment of Online Businessmen in Paciran District, Lamongan Regency through Product Photo Training

Online business has become the famous of society lately,especially since the pandemic hit the economic sector. Onlinebusiness has proven to be able to save many householdsfrom economic hardship. One of the online businessstrategies is to upload promotional statuses that displayproduct photos on their social media. The condition ofbuyers and sellers who do not meet physically makes the roleof product photos in online business very important.However, not all online businesses have eye-catchingproduct photos, especially online businesses who sellhandmade products on a household scale. The focus of thisservice focuses on empowering online businesses, especiallythose selling handmade products on a household scale sothat they are able to produce attractive product photos sothat they can exist and compete on social media in particular.This service is in the form of online product photo trainingusing a smartphone. Within ten days, the service participantswere proven to be able to produce quality product photoswith several compositions and angles.

Commentary: What the eye sees, Let's make the world see - Smart evolution of teleophthalmology.

Commentary: What the eye sees, Let's make the world see - Smart evolution of teleophthalmology.

Smartphone-enabled retinal arteriovenous imaging and correlation with coronary SYNTAX score

ObjectiveTo assess the feasibility of measurement of retinal arteriovenous (AV) ratio using a smartphone, we performed a comparative evaluation with fundus camera imaging and coronary SYNTAX score. MethodSuccessive coronary artery disease (CAD) patients who underwent coronary angiography were recruited for smartphone retinal imaging. Following pupillary dilatation, fundus camera images and smartphone photography were performed. Video images were captured with a smartphone, edited and analysed. Retinal artery and vein size at 0.5 and 1 disc diameter (DD) were measured using DICOM software by two independent observers. Another observer calculated SYNTAX score. ResultsAnalysable smartphone images were available in 91 (89.2%) of 102 patients. Tobacco use was found in 26%, hypertension in 54%, diabetes in 55%, and high LDL cholesterol in 50%. Median and 25–75 interquartile range (IQR) AV ratio at 0.5 and 1.0 DD, respectively, with smartphone were 0.48 (0.45–0.52) and 0.47 (0.45–0.52) and fundus camera were 0.48 (0.44–0.53) and 0.48 (0.45–0.53) (Spearman's correlation 0.80 and 0.79, p < 0.001). Coronary single vessel disease was in 21%, double vessel in 16%, triple vessel in 55%, normal angiogram in 8%, and median SYNTAX score was 18.0 (8.0–25.0). There was an inverse correlation of SYNTAX score with smartphone-derived AV ratio at 0.5 and 1.0 DD (rho −0.27,p = 0.007 and −0.26,p = 0.009) as well as with fundus camera (rho −0.37 and −0.38, p < 0.001). Trend-analysis showed an inverse association of smartphone AV ratio with increasing CAD (ptrend <0.001). ConclusionsSmartphone-based retinal AV imaging is feasible and comparable to fundus-camera imaging. There is a significant inverse correlation with coronary angiographic severity.

Corneal endothelial cell photography: comparison of smartphones

The documentation of ophthalmologic findings using smartphone photography can confirm diagnoses and enable follow-up assessments in outpatient care. Photographing corneal endothelial cells using asmartphone on aslit lamp is complex for both smartphone and examiner. Smartphone models differ in their ability to quickly and safely take images of the corneal endothelium. In this paper different smartphone models are presented with respect to their applicability for corneal endothelial cell photography and success factors for good smartphone imaging are described. In across-sectional study, aselection of 16different smartphone models were compared with respect to their use in corneal endothelial cell photography. The smartphones were attached to the slit lamp eyepiece using an adjustable adapter. It was tested whether high-quality endothelial cell images of healthy subjects could be obtained within 3 min using the standard photo app of the respective smartphone. In addition, the subjective difficulty of creating the image was recorded. Factors contributing to successful imaging of corneal endothelial cells were summarized in afigure. Distinct differences regarding feasibility and quality of endothelial cell photography were detected between the different smartphones. Not every smartphone is suitable for endothelial cell photography.

Reliability of Smart Phone Photographs for School Eye Screening.

Smartphone photographs capturing Bruckner’s reflex have demonstrated reliability in identifying amblyogenic conditions in children. Assessing visual acuity for screening has been the traditional method since the inception of school screening. The present study aims to assess the reliability of smartphone photographs in detecting ocular morbidities in school children and to compare it with traditional vision screening. Two thousand five hundred and twenty school children underwent vision screening and smartphone cameraphotography by a trained research assistant followed by a comprehensive eye examination of all children by an ophthalmologist. Children with unaided visual acuity less than 6/12 in either of the eyes were graded as abnormal. Based upon the characteristics of the Bruckner’s reflex, the photographs were graded as normal or abnormal by two investigators blinded to the clinical findings. Statistical analysis was performed to compare the sensitivity and specificity of traditional vision screening and photograph based screening, considering comprehensive eye examination as the gold standard. The sensitivity and specificity of vision screening was 81.88% and 97.35% whereas for photographs it was 94.69% and 98.85% respectively. When the two methods were compared, the p value was <0.05. We conclude that smartphone photography is better than traditional vision screening for detecting ocular morbidities in school children.

Integrated MPCAM: Multi-PSF learning for large depth-of-field computational imaging

Large DOF (depth-of-field) imaging with high SNR (signal-noise-ratio) is useful for applications such as machine vision and medical imaging. In traditional optical systems, DOF extension is always implemented at the cost of SNR. In this paper, we present a MPCAM (Multi-PSF Camera) system highly integrated with AF (auto-focus) function to realize both large DOF and high SNR imaging. MPCAM based on MPGAN (Multi-PSF Generative Adversarial Network) is first proposed to automatically extract multiple PSFs (point spread functions) and realize high fidelity image reconstruction by features fusion. The proposed end-to-end generative image fusion network is flexible and can be designed with different input dimensions for a given AF application, which is vital to circumvent the trade-off between DOF and SNR. We build a dataset containing 5000 raw images tailored to the proposed network by an off-the-shelf camera. Results show that our MPCAM system can produce images with average higher values than raw images over 4.625, and 0.061 in PNSR (peak signal to noise ratio), and SSIM (structure similarity) metrics, respectively. Moreover, compared to the classic and latest image fusion methods, the results also verify that our method has achieved comparable or even better performance. Due to its advance in high SNR and large DOF imaging, this novel, portable and inexpensive system is suitable for computational applications such as microscopic pathological diagnosis, domain-specific computational imaging and smartphone photography. The implementation code of MPGAN and dataset are available from https://www.kaggle.com/datasets/ktd970903/multi-psf-camera.

Large depth-of-field computational imaging with multi-spectral and dual-aperture optics.

Large DOF (depth-of-field) with high SNR (signal-noise-ratio) imaging is a crucial technique for applications from security monitoring to medical diagnostics. However, traditional optical design for large DOF requires a reduction in aperture size, and hence with a decrease in light throughput and SNR. In this paper, we report a computational imaging system integrating dual-aperture optics with a physics-informed dual-encoder neural network to realize prominent DOF extension. Boosted by human vision mechanism and optical imaging law, the dual-aperture imaging system is consisted of a small-aperture NIR camera to provide sharp edge and a large-aperture VIS camera to provide faithful color. To solve the imaging inverse problem in NIR-VIS fusion with different apertures, a specific network with parallel double encoders and the multi-scale fusion module is proposed to adaptively extract and learn the useful features, which contributes to preventing color deviation while preserving delicate scene textures. The proposed imaging framework is flexible and can be designed in different protos with varied optical elements for different applications. We provide theory for system design, demonstrate a prototype device, establish a real-scene dataset containing 3000 images, perform elaborate ablation studies and conduct peer comparative experiments. The experimental results demonstrate that our method effectively produces high-fidelity with larger DOF range than input raw images about 3 times. Without complex optical design and strict practical limitations, this novel, intelligent and integratable system is promising for variable vision applications such as smartphone photography, computational measurement, and medical imaging.

Food photography training with natural light concept using smartphone at SMKN 2 Cirebon City

SMKN 2 Cirebon City's culinary classes produce a variety of food products. However, these products are just displayed and evaluated, though. Therefore, this community service's goal is to improve students' smartphone photography skills as well as their knowledge of photography that makes use of natural light. The method of implementing this program is consultation and training. The program's outcomes include improved smartphone usage abilities and an increase in students' knowledge of photography using natural light to capture images of their learning-based culinary creations.