Edge adaptive filtering of depth maps for mobile devices

Abstract. Mobile phone cameras have an almost unlimited depth of field, and therefore the images captured with them have wide areas in focus. When the depth of field is digitally manipulated through image processing, accurate perception of depth in a captured scene is important. Capturing depth data requires advanced imaging methods. In case a stereo lens system is used, depth information is calculated from the disparities between stereo frames. The resulting depth map is often noisy or doesn’t have information for every pixel. Therefore it has to be filtered before it is used for emphasizing depth. Edges must be taken into account in this process to create natural-looking shallow depth of field images. In this study five filtering methods are compared with each other. The main focus is the Fast Bilateral Solver, because of its novelty and high reported quality. Mobile imaging requires fast filtering in uncontrolled environments, so optimizing the processing time of the filters is essential. In the evaluations the depth maps are filtered, and the quality and the speed is determined for every method. The results show that the Fast Bilateral Solver filters the depth maps well, and can handle noisy depth maps better than the other evaluated methods. However, in mobile imaging it is slow and needs further optimization.Reunatietoinen syvyyskarttojen suodatus mobiililaitteilla. Tiivistelmä. Matkapuhelimien kameroissa on lähes rajoittamaton syväterävyysalue, ja siksi niillä otetuissa kuvissa laajat alueet näkyvät tarkennettuina. Digitaalisessa syvyysterävyysalueen muokkauksessa tarvitaan luotettava syvyystieto. Syvyysdatan hankinta vaatii edistyneitä kuvausmenetelmiä. Käytettäessä stereokameroita syvyystieto lasketaan kuvien välisistä dispariteeteista. Tuloksena syntyvä syvyyskartta on usein kohinainen, tai se ei sisällä syvyystietoa joka pikselille. Tästä syystä se on suodatettava ennen käyttöä syvyyden korostamiseen. Tässä prosessissa reunat ovat otettava huomioon, jotta saadaan luotua luonnollisen näköisiä kapean syväterävyysalueen kuvia. Tässä tutkimuksessa verrataan viittä suodatusmenetelmää keskenään. Eniten keskitytään nopeaan bilateraaliseen ratkaisijaan, johtuen sen uutuudesta ja korkeasta tuloksen laadusta. Mobiililaitteella kuvantamisen vaatimuksena on nopea suodatus hallitsemattomissa olosuhteissa, joten suodattimien prosessointiajan optimointi on erittäin tärkeää. Vertailuissa syvyyskuvat suodatetaan ja suodatuksen laatu ja nopeus mitataan jokaiselle menetelmälle. Tulokset osoittavat, että nopea bilateraalinen ratkaisija suodattaa syvyyskarttoja hyvin ja osaa käsitellä kohinaisia syvyyskarttoja paremmin kuin muut tarkastellut menetelmät. Mobiilikuvantamiseen se on kuitenkin hidas ja tarvitsee pidemmälle menevää optimointia

Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair

All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultrawide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras.Comment: Early Access by IEEE Transactions on Circuits and Systems for Video Technology 202

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

Between Freedom and Fixity: Artistic Reflections on Composition and Improvisation

Between Freedom and Fixity: Artistic Reflections on Composition and Improvisation is a practice-based research project that aims to highlight the role of freedom and fixity in music and to develop a discourse based on these two concepts. The research suggests a creative approach based on the interrelationship between freedom and fixity, for example their combination, juxtaposition, and tension, and describes them as abstractions or placeholders for musical agents such as rhythm, notes, structure, timeline, and interactive computer systems. Another important notion is the inherent coexistence of the two concepts, and proposing the constant oscillating between them as a creative musical approach. Furthermore, the research aims to establish the use of freedom and fixity as a productive paths for extra-musical disciplines. Four works by the author are used as case studies to examine the integration of the research concepts as tangible musical forms. In each of the case studies the concepts are embodied differently, thus different relationships develop between them in each piece. This study relies on the author's experience and experimentation as a composer, performer, improviser, and electronic-music practitioner, and draws inspiration from works by other musicians and scholars.Research in and through artistic practic