Real Photographs Research Articles

MDTNet: Partial transformer with degradation-aware module for restoring old photos with multiple degradations

Old photos preserve the memories of the old days and have special meanings in life. However, they often suffer from severe and complex degradations, such as cracks, dirt, and noise. Old photo restoration is thus a meaningful but challenging task. Most of the current deep learning methods to repair corrupted old photos focus on one single type of degradation or limited number of degradations. The lack of proper old photo datasets makes the digital restoration problem even more challenging. In this paper, we propose MDTNet, which is a novel transformer-based method for restoring old photos with multiple degradations. MDTNet is an efficient end-to-end transformer architecture with only a single encoder and a decoder to complete the restoration. Specifically, a novel partial transformer encoder is designed with partial multi-head self-attention to extract local-global context information from the valid masked area, which represents the unbroken area. In addition, a degradation-aware module is proposed in the decoder to automatically learn different degradation information, to improve the effect of multiple degradation restoration. Moreover, a synthetic old photo dataset — the SynOld dataset — was built, which simulates the multiple degradations of real old photos. Experimental results show that the proposed method outperforms the state-of-the-art methods on both public datasets and the custom-developed dataset. The code is available at https://github.com/YuanZhaoc/MDTNet.

Laying the foundations for using generative AI images in architectural research: do images convey the intended spaces and ambiances?

The advent of Generative Artificial Intelligence (GenAI) models, such as Stable Diffusion, Open AI's DALL-E2, and MidJourney, has opened the door to a huge range of new possibilities in architectural research and practice. Before architects and researchers can fully leverage these models, it is crucial to assess their proficiency in generating images that accurately depict real spaces and ambiances. Here, we assessed the proficiency of DALL-E2 in depicting intended spaces and intended ambiances, using real photographs from Google Images as a comparative benchmark. Images of eighteen distinct home spaces, each intended to evoke three different ambiances, were generated and presented to human observers. The images were evaluated in terms of their perceived realism, their accuracy in depicting the space and ambiance intended. Findings highlight GenAI's capability in depicting spaces and point to its potential in convincingly conveying ambiances. Beyond the substantive findings, this research highlights the importance of evaluating the rapidly emerging tools enabled by recent developments in AI.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

SCAWA scales: A new digital tool for wrinkles clinical grading based on AI.

Clinical assessment of wrinkle depth is essential for efficacy evaluations of anti-ageing products. Standardized photographic scales, representative of different wrinkle depths are often used by experts to assign subjects reliable grades. These tools, based on real pictures, usually exist as hard copies (printed books or sheets) for invivo gradings. Our project aims at developing a methodology to create digital standardized computer-generated scales, allowing photograph and real-life gradings, and providing raters with greater comfort, accessibility, and flexibility in their construction, thanks to the artificial intelligence significative contribution. A completely new approach, based on machine learning, allows the creation of Standardized ColorFace® AI-based Wrinkle Assessment (SCAWA) scales. Instead of using real photographs, the scale images are computer-generated. A generative adversarial network (GAN) is trained to create realistic wrinkle samples that are finely controllable by exploring the GAN latent space. Finally, the scale images are selected among hundreds of artificial images depicting natural wrinkle appearances, such as the illustrated wrinkle evolution is well-detailed (small gaps between grades), morphologically stable, and mathematically linear according to a criterion of wrinkle conspicuous depth. The created 12-point scale for crow's feet wrinkle evaluation on ColorFace® pictures is proven to be realistic, linear, and robustly and accurately usable for photograph assessments. The scale coherence in terms of image ranking has been validated, as well as its reliability and acceptability in real conditions of use. Additionally, the wrinkle grades obtained by the SCAWA scale are well correlated (R = 0.94) with the ones obtained by the Skin Aging Atlas on the same pictures. The AI methodology and digital format brought also interesting side results, such as an enhanced harmonization between experts and a higher representativeness, that is, a decrease of out-of-range pictures. SCAWA scale makes the most of machine learning to provide an innovative digital tool to ease wrinkles in visual assessment of pictures, while optimizing linearity, homogeneity, and accuracy aspects. The experts' enthusiastic feedback about the scale format and quality is promising regarding the adaptation of the methodology to other signs and a larger distribution of this tool in the market of cosmetic product efficacy assessment.

Synthesis of Comic-Style Portraits Using Combination of CycleGAN and Pix2Pix

This paper proposes a system for converting face photos into portraits with specific charcoal sketch-like style. NPR (non-photorealistic rendering) for converting real photo images into anime-style images have been studied. Its promising application is the creation of users’ portraits on social networks for preventing leaks of personal information. An image-to-image transformation using GAN (Generative Adversarial Network), such as CycleGAN or Pix2Pix, is expected to be used for this goal. However, it is difficult to generate portraits with specific comic styles that satisfy two conditions at the same time: preserving photorealistic features and reproducing the comic styles. For example, replacing too many photorealistic features on the face with comic style will destroy users’ identity, such as eyes shape. To solve this problem, the proposed system combines CycleGAN and Pix2Pix. CycleGAN is used to generate paired examples for Pix2Pix and Pix2Pix learns photo-to-comic transformation. To further emphasize a comic style, this paper also proposes two extensions, which divides face images into several parts such as hair and eye parts. The quality of generated images are evaluated with questionnaire by 50 answerers. The results show that the images generated by the proposed system are highly evaluated than the images generated by CycleGAN in terms of preserving the features in a photo and reproducing a comic style. Although the segmentation tend to collapse the generated images, it can reproduce a comic style effectively, especially on facial parts.

Factors Associated With Pain Perception and Management Among Paediatric Nurses and Nursing Students Using Virtual Children: A Comparative Study.

To identify and compare factors associated with pain perception and management among paediatric nurses and nursing students using virtual human technology. This study employed a comparative, cross-sectional design. Fifty-one paediatric nurses from a tertiary hospital and 50 senior nursing students from a nursing college, both located in South Korea, participated in the study. Virtual vignettes, including scenarios with virtual children and their clinical information, were used to assess participants' pain perception and management. The virtual children, created from real face photos, varied in age (young/old), sex (boy/girl) and facial expressions (smile/grimace). Participants rated perceived pain scores and selected management choices for eight virtual vignettes, which were randomly presented. The Korean version of the Paediatric Nurses' Knowledge and Attitude Survey was completed along with demographic information. Both paediatric nurses and nursing students rated pain scores lower than the actual pain scores reported by virtual children. Nurses rated higher pain scores and accordingly selected more medication doses compared to students. Beyond pain-related knowledge, the facial expression of the virtual child influenced pain rating and medication choices, while age and sex had no effect. Nursing students were more sensitive to the facial expressions of virtual children when rating their pain compared to nurses. Future studies should explore how paediatric nurses' clinical experiences impact their assessment and management of child patient pain. Paediatric nurses' knowledge of pain is inadequate, necessitating ongoing education in pain management. Both nurses and nursing students should self-reflect on their pain assessment practices to ensure unbiased care regardless of child patients' characteristics. Virtual human technology can be utilised to train nurses and students to identify and address any biases in their assessment of patients' pain perception. STROBE checklist, cross-sectional. Digital face photos of four healthy children were used as the fundamental material for creating virtual children, with their parents' consent.

Studying the Effect of Material and Geometry on Perceptual Outdoor Illumination.

Understanding and modeling perceived properties of sky-dome illumination is an important but challenging problem due to the interplay of several factors such as the materials and geometries of the objects present in the scene being observed. Existing models of sky-dome illumination focus on the physical properties of the sky. However, these parametric models often do not align well with the properties perceived by a human observer. In this work, drawing inspiration from the Hosek-Wilkie sky-dome model, we investigate the perceptual properties of outdoor illumination. For this purpose, we perform a large-scale user study via crowdsourcing to collect a dataset of perceived illumination properties (scattering, glare, and brightness) for different combinations of geometries and materials under a variety of outdoor illuminations, totaling 5,000 distinct images. We perform a thorough statistical analysis of the collected data which reveals several interesting effects. For instance, our analysis shows that when there are objects in the scene made of rough materials, the perceived scattering of the sky increases. Furthermore, we utilize our extensive collection of images and their corresponding perceptual attributes to train a predictor. This predictor, when provided with a single image as input, generates an estimation of perceived illumination properties that align with human perceptual judgments. Accurately estimating perceived illumination properties can greatly enhance the overall quality of integrating virtual objects into real scene photographs. Consequently, we showcase various applications of our predictor. For instance, we demonstrate its utility as a luminance editing tool for showcasing virtual objects in outdoor scenes.

Structural visualization analysis applied to the preservation of architectural heritage: the case of stilted houses in southwest Hubei, China

Wooden buildings represent a unique aspect of China’s architectural heritage. However, over time, these buildings have suffered varying degrees of structural damage, particularly those located in China’s mountainous regions. Frequent natural disasters and inconvenient transportation further exacerbate the vulnerability of these structures. This study focuses on the typical wooden stilt houses found in the Wuling Mountain area of southwest Hubei. The different forms of columns and beams, as well as the overall structural symmetry of the stilt houses, were classified into five common structural types. Precise digital models were established via real photos and collected dimensional data, and these five models were evaluated for static, buckling, utilization, and ultimate limit states. The results indicate that: (1) overall symmetrical structures perform better than asymmetrical ones; (2) structures where all columns are grounded and act as support columns exhibit the best overall mechanical performance; and (3) transverse-tie beams(chuanfang) primarily serve a connecting role rather than a load-bearing role, thus an excess of transverse-tie beams(chuanfang) can reduce the stability of the building structure. Based on the analysis results, targeted protective measures and recommendations were proposed and verified through structural evaluations. These initiatives provide new methods and insights for the protection of architectural heritage.

Detecting images generated by diffusers.

In recent years, the field of artificial intelligence has witnessed a remarkable surge in the generation of synthetic images, driven by advancements in deep learning techniques. These synthetic images, often created through complex algorithms, closely mimic real photographs, blurring the lines between reality and artificiality. This proliferation of synthetic visuals presents a pressing challenge: how to accurately and reliably distinguish between genuine and generated images. This article, in particular, explores the task of detecting images generated by text-to-image diffusion models, highlighting the challenges and peculiarities of this field. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple multi-layer perceptrons (MLPs), starting from features extracted by CLIP or RoBERTa, or using traditional convolutional neural networks (CNNs). These latter models achieve remarkable performances in particular when pretrained on large datasets. We also observe that models trained on images generated by Stable Diffusion can occasionally detect images generated by GLIDE, but only on the MSCOCO dataset. However, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images in some cases can lead to a better generalization capability, especially if textual features are closely related to visual ones. We also discovered that the type of subject depicted in the image can significantly impact performance. This work provides insights into the feasibility of detecting generated images and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers.

A multimodal machine learning approach to generate news articles from geo-tagged images

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.

Visual perception of group fatness as a precursor to social identity threat and safety

ABSTRACT Across two studies (N = 190), we evaluate how fat people perceive groups of bodies and how this visual processing predicts social identity threat and safety. We examined how fat people ensemble code – rapidly and simultaneously extract visual information from a group – body size from groups of bodies. Stimuli included sets of 3-d modeled bodies varying in body mass index (BMI; Study 1) and real photographs of groups of people with known BMI (Study 2). As hypothesized, we found that fat people were generally accurate at ensemble coding the average body size of arrays after 500 ms exposure, and that, particularly among fat people with medium and high stigma consciousness, arrays of increasing average BMI were associated with heightened feelings of identity safety. Our work suggests that the representation of fatness in one’s environment may be an important precursor to social identity threat and safety processes.

Visualisation in architecture – opportunities and threats

Development of computer technology, computing power of graphics cards and processors allows, with the use of appropriate software, to create extremely realistic three-dimensional images. The effects of generated visualisations can be difficult to distinguish from real photos of objects. Architects often verify the fulfilment of design goals based on a rendered digital image. Unfortunately, designers are often not fully aware of the limitations and simplifications of the tools used to create visualisations. An uncritical approach to the presented results may lead to design errors. This article presents the possibilities of visualisation in selected computer programs used by architects and cases in which correct visualisation is possible only with the use of specialised software. The article pays special attention to the possibility of errors in the visualisation of architectural lighting.

Predicting Perceived Gloss: Do Weak Labels Suffice?

AbstractEstimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fully‐understood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human‐annotated images for accurate perception predictions. Obtaining reliable annotations is a costly endeavor, aggravated by the limited ability of these models to generalise to different aspects of appearance. In this work, we show how a much smaller set of human annotations (“strong labels”) can be effectively augmented with automatically derived “weak labels” in the context of learning a low‐dimensional image‐computable gloss metric. We evaluate three alternative weak labels for predicting human gloss perception from limited annotated data. Incorporating weak labels enhances our gloss prediction beyond the current state of the art. Moreover, it enables a substantial reduction in human annotation costs without sacrificing accuracy, whether working with rendered images or real photographs.

A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement

Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique depends on augmentation of swarm intelligence via using Cat Swarm Optimization algorithm (CSO). The swarm intelligence is used to obtain the optimal structure of CLAHE technique. Tomato plant images have used and applied as dataset because of its important and influence in our life. For fair analysis of two techniques, Absolute Mean Brightness Error (AMBE), peak signal-to-noise ratio (PSNR), entropy and Contrast Gain of fundus images are analyzed by using MATLAB. The results show that performance of the proposed technique reveals the efficiently and robustness when compared results of standard technique.

Realness of face images can be decoded from non-linear modulation of EEG responses

Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face’s eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.

Challenges in geometric modelling–A comparison of students’ mathematization with real objects, photos, and 3D models

Mathematical modelling aims at contributing to the involvement of reality in mathematics education. As an example, geometric modelling can be implemented by the use of real objects in modelling tasks. Still, (geometric) modelling tasks can be a challenge for students, especially in the transfer from reality to mathematics, which is referred to as mathematization. Since the representation of a real object in tasks might differ, the question arises, which challenges can be observed when working in different task settings. In a study with 19 secondary school students, the task settings (1) outdoors at the real object, (2) indoors with photos of the real object, and (3) indoors with a 3D model of the real object are compared. Based on video recordings, differences concerning the students’ challenges are examined. The results highlight challenges in estimating and measuring when working at the real object, scale and perspective when working with photos and the transfer between representation and object when working with 3D models.

Performance evaluation of generative adversarial networks for generating mugshot images from text description

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.

Investigating the role of Emotions on the learning effectiveness in VR enabled teaching interventions in school

Technology advancements are known to frequently have direct and significant effects on the educational sector. There is an increasing trend in the academic use of Virtual Reality to demonstrates usability, utility, and amusement of the learner. The use of technological tools like virtual reality (VR) helps people develop the digital-age literacy, creative thinking, communication, teamwork, and problem-solving abilities that are required to transform information rather than merely consume it. VR improves standard courses to accommodate students' various learning demands. Applications for skill mastery and training are now possible because to recent advancements in digital reality generation. The learning is made more enjoyable and productive for the user by using real like photos, videos, animations etc., which provide augmented knowledge about the surroundings. Learners commitment is a significant predictor of attainment, retention and diligence for enhanced learning. The research papers findings reported that the VR environment increases the learning outcomes and offer many benefits for devoting time and money to them in higher and tertiary educational settings. For current research 347 students had been assigned to VR technology for learning. The outcome of this research supports the objective that the emotions affect learning using the VR technologies. The results revealed that the positive emotions affect the Ergonomic, Learning, Hedonic and overall learning. Emotions influence students' desire to study, based on their eagerness, inquisitiveness and willingness to accomplishing their goals of positive outcomes. Emotions make the implementation of learning approaches easier. Additionally, emotions can influence self-control techniques.

Real is the new sexy: the influence of perceived realness on self-reported arousal to sexual visual stimuli

ABSTRACT As state-of-art technology can create artificial images that are indistinguishable from real ones, it is urgent to understand whether believing that a picture is real or not has some import over affective phenomena such as sexual arousal. Thus, in two pre-registered online studies, we tested whether 60 images depicting models in underwear elicited higher self-reported sexual arousal when believed to be (N = 57) or presented as (N = 108) real photographs as opposed to artificially generated. In both cases, Realness correlated with significantly higher scores on self-reported sexual arousal. Consistently with the literature on downregulation of emotional response to fictional works, our result indicates that sexual images that are perceived to be fake are less arousing than those believed to portray real people.

Pseudophotographs in Children’s Literature: A New Object for Photoliterature Studies

This article proposes to theorize the study of pseudophotographs as a subtype of photoliterature studies. Pseudophotographs are any illustrations understood within the context of a picturebook or graphic novel to be representations of photographs. This article attempts to define this undertheorized graphic object, to discuss its various functions, and to analyse what pseudophotographs can tell us about how real photographs and photography are considered in children’s literature. Because pseudophotographs appropriate some of the conventions of photography and yet are unlike real photographs in many ways, they are, we argue, a significant place for implicit discourse about the art and practice of photography and about affective encounters with photographs.