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

デバイスの限界を超えた正確な撮像を可能にする深層学習

Tohoku University博士（情報科学）thesi

Holoscopic 3D image depth estimation and segmentation techniques

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonToday’s 3D imaging techniques offer significant benefits over conventional 2D imaging techniques. The presence of natural depth information in the scene affords the observer an overall improved sense of reality and naturalness. A variety of systems attempting to reach this goal have been designed by many independent research groups, such as stereoscopic and auto-stereoscopic systems. Though the images displayed by such systems tend to cause eye strain, fatigue and headaches after prolonged viewing as users are required to focus on the screen plane/accommodation to converge their eyes to a point in space in a different plane/convergence. Holoscopy is a 3D technology that targets overcoming the above limitations of current 3D technology and was recently developed at Brunel University. This work is part W4.1 of the 3D VIVANT project that is funded by the EU under the ICT program and coordinated by Dr. Aman Aggoun at Brunel University, West London, UK. The objective of the work described in this thesis is to develop estimation and segmentation techniques that are capable of estimating precise 3D depth, and are applicable for holoscopic 3D imaging system. Particular emphasis is given to the task of automatic techniques i.e. favours algorithms with broad generalisation abilities, as no constraints are placed on the setting. Algorithms that provide invariance to most appearance based variation of objects in the scene (e.g. viewpoint changes, deformable objects, presence of noise and changes in lighting). Moreover, have the ability to estimate depth information from both types of holoscopic 3D images i.e. Unidirectional and Omni-directional which gives horizontal parallax and full parallax (vertical and horizontal), respectively. The main aim of this research is to develop 3D depth estimation and 3D image segmentation techniques with great precision. In particular, emphasis on automation of thresholding techniques and cues identifications for development of robust algorithms. A method for depth-through-disparity feature analysis has been built based on the existing correlation between the pixels at a one micro-lens pitch which has been exploited to extract the viewpoint images (VPIs). The corresponding displacement among the VPIs has been exploited to estimate the depth information map via setting and extracting reliable sets of local features. ii Feature-based-point and feature-based-edge are two novel automatic thresholding techniques for detecting and extracting features that have been used in this approach. These techniques offer a solution to the problem of setting and extracting reliable features automatically to improve the performance of the depth estimation related to the generalizations, speed and quality. Due to the resolution limitation of the extracted VPIs, obtaining an accurate 3D depth map is challenging. Therefore, sub-pixel shift and integration is a novel interpolation technique that has been used in this approach to generate super-resolution VPIs. By shift and integration of a set of up-sampled low resolution VPIs, the new information contained in each viewpoint is exploited to obtain a super resolution VPI. This produces a high resolution perspective VPI with wide Field Of View (FOV). This means that the holoscopic 3D image system can be converted into a multi-view 3D image pixel format. Both depth accuracy and a fast execution time have been achieved that improved the 3D depth map. For a 3D object to be recognized the related foreground regions and depth information map needs to be identified. Two novel unsupervised segmentation methods that generate interactive depth maps from single viewpoint segmentation were developed. Both techniques offer new improvements over the existing methods due to their simple use and being fully automatic; therefore, producing the 3D depth interactive map without human interaction. The final contribution is a performance evaluation, to provide an equitable measurement for the extent of the success of the proposed techniques for foreground object segmentation, 3D depth interactive map creation and the generation of 2D super-resolution viewpoint techniques. The no-reference image quality assessment metrics and their correlation with the human perception of quality are used with the help of human participants in a subjective manner

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Light Field compression and manipulation via residual convolutional neural network

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF\u27s, using simulation, it is possible to explore a high-quality captured LF\u27s properties. The immediate concerns following the LF capture are its storage and processing time. A rich LF occupies a large chunk of memory ---order of multiple gigabytes per LF---. Also, most feature extraction techniques associated with LF postprocessing involve multi-dimensional integration that requires access to the whole LF and is usually time-consuming. Recent advancements in computer processing units made it possible to simulate realistic images using physical-based rendering software. In this work, at first, a transformation function is proposed for building a camera array (CA) to capture the same portion of LF from a scene that a standard plenoptic camera (SPC) can acquire. Using this transformation, LF simulation with similar properties as a plenoptic camera will become trivial in any rendering software. Artificial intelligence (AI) and machine learning (ML) algorithms ---when deployed on the new generation of GPUs--- are faster than ever. It is possible to generate and train large networks with millions of trainable parameters to learn very complex features. Here, residual convolutional neural network (RCNN) structures are employed to build complex networks for compression and feature extraction from an LF. By combining state-of-the-art image compression and RCNN, I have created a compression pipeline. The proposed pipeline\u27s bit per pixel (bpp) ratio is 0.0047 on average. I show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs. In the end, using RCNN, I created a network called RefNet, for extracting a group of 16 refocused images from a raw LF. The training parameters of the 16 LFs are set to (\alpha=0.125, 0.250, 0.375, ..., 2.0) for training. I show that RefNet is 134x faster than the state-of-the-art refocusing technique. The RefNet is also superior in color prediction compared to the state-of-the-art ---Fourier slice and shift-and-sum--- methods

Enhanced processing methods for light field imaging

The light field camera provides rich textural and geometric information, but it is still challenging to use it efficiently and accurately to solve computer vision problems. Light field image processing is divided into multiple levels. First, low-level processing technology mainly includes the acquisition of light field images and their preprocessing. Second, the middle-level process consists of the depth estimation, light field encoding, and the extraction of cues from the light field. Third, high-level processing involves 3D reconstruction, target recognition, visual odometry, image reconstruction, and other advanced applications. We propose a series of improved algorithms for each of these levels. The light field signal contains rich angular information. By contrast, traditional computer vision methods, as used for 2D images, often cannot make full use of the high-frequency part of the light field angular information. We propose a fast pre-estimation algorithm to enhance the light field feature to improve its speed and accuracy when keeping full use of the angular information.Light field filtering and refocusing are essential cues in light field signal processing. Modern frequency domain filtering technology and wavelet technology have effectively improved light field filtering accuracy but may fail at object edges. We adapted the sub-window filtering with the light field to improve the reconstruction of object edges. Light field images can analyze the effects of scattering and refraction phenomena, and there are still insufficient metrics to evaluate the results. Therefore, we propose a physical rendering-based light field dataset that simulates the distorted light field image through a transparent medium, such as atmospheric turbulence or water surface. The neural network is an essential method to process complex light field data. We propose an efficient 3D convolutional autoencoder network for the light field structure. This network overcomes the severe distortion caused by high-intensity turbulence with limited angular resolution and solves the difficulty of pixel matching between distorted images. This work emphasizes the application and usefulness of light field imaging in computer vision whilst improving light field image processing speed and accuracy through signal processing, computer graphics, computer vision, and artificial neural networks