Automatic image quality enhancement using deep neural networks

Abstract. Photo retouching can significantly improve image quality and it is considered an essential part of photography. Traditionally this task has been completed manually with special image enhancement software. However, recent research utilizing neural networks has been proven to perform better in the automated image enhancement task compared to traditional methods. During the literature review of this thesis, multiple automatic neural-network-based image enhancement methods were studied, and one of these methods was chosen for closer examination and evaluation. The chosen network design has several appealing qualities such as the ability to learn both local and global enhancements, and its simple architecture constructed for efficient computational speed. This research proposes a novel dataset generation method for automated image enhancement research, and tests its usefulness with the chosen network design. This dataset generation method simulates commonly occurring photographic errors, and the original high-quality images can be used as the target data. This dataset design allows studying fixes for individual and combined aberrations. The underlying idea of this design choice is that the network would learn to fix these aberrations while producing aesthetically pleasing and consistent results. The quantitative evaluation proved that the network can learn to counter these errors, and with greater effort, it could also learn to enhance all of these aspects simultaneously. Additionally, the network’s capability of learning local and portrait specific enhancement tasks were evaluated. The models can apply the effect successfully, but the results did not gain the same level of accuracy as with global enhancement tasks. According to the completed qualitative survey, the images enhanced by the proposed general enhancement model can successfully enhance the image quality, and it can perform better than some of the state-of-the-art image enhancement methods.Automaattinen kuvanlaadun parantaminen käyttämällä syviä neuroverkkoja. Tiivistelmä. Manuaalinen valokuvien käsittely voi parantaa kuvanlaatua huomattavasti ja sitä pidetään oleellisena osana valokuvausprosessia. Perinteisesti tätä tehtävää varten on käytetty erityisiä manuaalisesti operoitavia kuvankäsittelyohjelmia. Nykytutkimus on kuitenkin todistanut neuroverkkojen paremmuuden automaattisessa kuvanparannussovelluksissa perinteisiin menetelmiin verrattuna. Tämän diplomityön kirjallisuuskatsauksessa tutkittiin useita neuroverkkopohjaisia kuvanparannusmenetelmiä, ja yksi näistä valittiin tarkempaa tutkimusta ja arviointia varten. Valitulla verkkomallilla on useita vetoavia ominaisuuksia, kuten paikallisten sekä globaalien kuvanparannusten oppiminen ja sen yksinkertaistettu arkkitehtuuri, joka on rakennettu tehokasta suoritusnopeutta varten. Tämä tutkimus esittää uuden opetusdatan generointimenetelmän automaattisia kuvanparannusmetodeja varten, ja testaa sen soveltuvuutta käyttämällä valittua neuroverkkorakennetta. Tämä opetusdatan generointimenetelmä simuloi usein esiintyviä valokuvauksellisia virheitä, ja alkuperäisiä korkealaatuisia kuvia voi käyttää opetuksen tavoitedatana. Tämän generointitavan avulla voitiin tutkia erillisten valokuvausvirheiden, sekä näiden yhdistelmän korjausta. Tämän menetelmän tarkoitus oli opettaa verkkoa korjaamaan erilaisia virheitä sekä tuottamaan esteettisesti miellyttäviä ja yhtenäisiä tuloksia. Kvalitatiivinen arviointi todisti, että käytetty neuroverkko kykenee oppimaan erillisiä korjauksia näille virheille. Neuroverkko pystyy oppimaan myös mallin, joka korjaa kaikkia ennalta määrättyjä virheitä samanaikaisesti, mutta alhaisemmalla tarkkuudella. Lisäksi neuroverkon kyvykkyyttä oppia paikallisia muotokuvakohtaisia kuvanparannuksia arvioitiin. Koulutetut mallit pystyvät myös toteuttamaan paikallisen kuvanparannuksen onnistuneesti, mutta nämä mallit eivät yltäneet globaalien parannusten tasolle. Toteutetun kyselytutkimuksen mukaan esitetty yleisen kuvanparannuksen malli pystyy parantamaan kuvanlaatua onnistuneesti, sekä tuottaa parempia tuloksia kuin osa vertailluista kuvanparannustekniikoista

Improving Selfie Aesthetics with Interactive Guidance based on Empirical Models

We introduce RealSelfie, a smartphone camera application providing interactive guid- ance to help people take better self-portrait photos (commonly called “selfies”). The appli- cation uses empirical models to estimate aesthetic quality built from data gathered by 2,700 Amazon Mechanical Turk (AMT) aesthetic quality assessments of synthetic photographs. The synthetic photographs are generated from 3D models of realistic human models by manipulating a virtual camera and virtual lighting to precisely explore the space of three photographic principle parameters: face size, face position, and light direction. The Re- alSelfie application calculates the current value for each parameter using computer vision techniques and then compares those values with each model’s aesthetic estimates to display directional hints overlaid on the live camera preview. As part of this system, we contribute an algorithm to estimate lighting direction using the pattern of light and shade near the nose. We conduct a study to evaluate the RealSelfie application with 20 participants in a controlled environment to eliminate background and lighting confounds. AMT ratings of the photos show that RealSelfie provides a 26% increase in aesthetics over providing no guidance

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Physical sketching tools and techniques for customized sensate surfaces

Sensate surfaces are a promising avenue for enhancing human interaction with digital systems due to their inherent intuitiveness and natural user interface. Recent technological advancements have enabled sensate surfaces to surpass the constraints of conventional touchscreens by integrating them into everyday objects, creating interactive interfaces that can detect various inputs such as touch, pressure, and gestures. This allows for more natural and intuitive control of digital systems. However, prototyping interactive surfaces that are customized to users' requirements using conventional techniques remains technically challenging due to limitations in accommodating complex geometric shapes and varying sizes. Furthermore, it is crucial to consider the context in which customized surfaces are utilized, as relocating them to fabrication labs may lead to the loss of their original design context. Additionally, prototyping high-resolution sensate surfaces presents challenges due to the complex signal processing requirements involved. This thesis investigates the design and fabrication of customized sensate surfaces that meet the diverse requirements of different users and contexts. The research aims to develop novel tools and techniques that overcome the technical limitations of current methods and enable the creation of sensate surfaces that enhance human interaction with digital systems.Sensorische Oberflächen sind aufgrund ihrer inhärenten Intuitivität und natürlichen Benutzeroberfläche ein vielversprechender Ansatz, um die menschliche Interaktionmit digitalen Systemen zu verbessern. Die jüngsten technologischen Fortschritte haben es ermöglicht, dass sensorische Oberflächen die Beschränkungen herkömmlicher Touchscreens überwinden, indem sie in Alltagsgegenstände integriert werden und interaktive Schnittstellen schaffen, die diverse Eingaben wie Berührung, Druck, oder Gesten erkennen können. Dies ermöglicht eine natürlichere und intuitivere Steuerung von digitalen Systemen. Das Prototyping interaktiver Oberflächen, die mit herkömmlichen Techniken an die Bedürfnisse der Nutzer angepasst werden, bleibt jedoch eine technische Herausforderung, da komplexe geometrische Formen und variierende Größen nur begrenzt berücksichtigt werden können. Darüber hinaus ist es von entscheidender Bedeutung, den Kontext, in dem diese individuell angepassten Oberflächen verwendet werden, zu berücksichtigen, da eine Verlagerung in Fabrikations-Laboratorien zum Verlust ihres ursprünglichen Designkontextes führen kann. Zudem stellt das Prototyping hochauflösender sensorischer Oberflächen aufgrund der komplexen Anforderungen an die Signalverarbeitung eine Herausforderung dar. Diese Arbeit erforscht dasDesign und die Fabrikation individuell angepasster sensorischer Oberflächen, die den diversen Anforderungen unterschiedlicher Nutzer und Kontexte gerecht werden. Die Forschung zielt darauf ab, neuartigeWerkzeuge und Techniken zu entwickeln, die die technischen Beschränkungen derzeitigerMethoden überwinden und die Erstellung von sensorischen Oberflächen ermöglichen, die die menschliche Interaktion mit digitalen Systemen verbessern

FM radio: family interplay with sonic mementos

Digital mementos are increasingly problematic, as people acquire large amounts of digital belongings that are hard to access and often forgotten. Based on fieldwork with 10 families, we designed a new type of embodied digital memento, the FM Radio. It allows families to access and play sonic mementos of their previous holidays. We describe our underlying design motivation where recordings are presented as a series of channels on an old fashioned radio. User feedback suggests that the device met our design goals: being playful and intriguing, easy to use and social. It facilitated family interaction, and allowed ready access to mementos, thus sharing many of the properties of physical mementos that we intended to trigger

ClearPhoto - augmented photography

The widespread use of mobile devices has made known to the general public new areas that were hitherto confined to specialized devices. In general, the smartphone came to give all users the ability to execute multiple tasks, and among them, take photographs using the integrated cameras. Although these devices are continuously receiving improved cameras, their manufacturers do not take advantage of their full potential, since the operating systems normally offer simple APIs and applications for shooting. Therefore, taking advantage of this environment for mobile devices, we find ourselves in the best scenario to develop applications that help the user obtaining a good result when shooting. In an attempt to provide a set of techniques and tools more applied to the task, this dissertation presents, as a contribution, a set of tools for mobile devices that provides information in real-time on the composition of the scene before capturing an image. Thus, the proposed solution gives support to a user while capturing a scene with a mobile device. The user will be able to receive multiple suggestions on the composition of the scene, which will be based on rules of photography or other useful tools for photographers. The tools include horizon detection and graphical visualization of the color palette presented on the scenario being photographed. These tools were evaluated regarding the mobile device implementation and how users assess their usefulness

Gesture based interface for image annotation

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaGiven the complexity of visual information, multimedia content search presents more problems than textual search. This level of complexity is related with the difficulty of doing automatic image and video tagging, using a set of keywords to describe the content. Generally, this annotation is performed manually (e.g., Google Image) and the search is based on pre-defined keywords. However, this task takes time and can be dull. In this dissertation project the objective is to define and implement a game to annotate personal digital photos with a semi-automatic system. The game engine tags images automatically and the player role is to contribute with correct annotations. The application is composed by the following main modules: a module for automatic image annotation, a module that manages the game graphical interface (showing images and tags), a module for the game engine and a module for human interaction. The interaction is made with a pre-defined set of gestures, using a web camera. These gestures will be detected using computer vision techniques interpreted as the user actions. The dissertation also presents a detailed analysis of this application, computational modules and design, as well as a series of usability tests