Light Field Blind Motion Deblurring

We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields.Comment: To be presented at CVPR 201

Motion-corrected Fourier ptychography

Fourier ptychography (FP) is a recently proposed computational imaging technique for high space-bandwidth product imaging. In real setups such as endoscope and transmission electron microscope, the common sample motion largely degrades the FP reconstruction and limits its practicability. In this paper, we propose a novel FP reconstruction method to efficiently correct for unknown sample motion. Specifically, we adaptively update the sample's Fourier spectrum from low spatial-frequency regions towards high spatial-frequency ones, with an additional motion recovery and phase-offset compensation procedure for each sub-spectrum. Benefiting from the phase retrieval redundancy theory, the required large overlap between adjacent sub-spectra offers an accurate guide for successful motion recovery. Experimental results on both simulated data and real captured data show that the proposed method can correct for unknown sample motion with its standard deviation being up to 10% of the field-of-view scale. We have released our source code for non-commercial use, and it may find wide applications in related FP platforms such as endoscopy and transmission electron microscopy

Coded aperture and coded exposure photography : an investigation into applications and methods

This dissertation presents an introduction to the field of computational photography, and provides a survey of recent research. Specific attention is given to coded aperture and coded exposure theory and methods, as these form the basis for the experiments performed

Near-invariant blur for depth and 2D motion via time-varying light field analysis

Recently, several camera designs have been proposed for either making defocus blur invariant to scene depth or making motion blur invariant to object motion. The benefit of such invariant capture is that no depth or motion estimation is required to remove the resultant spatially uniform blur. So far, the techniques have been studied separately for defocus and motion blur, and object motion has been assumed 1D (e.g., horizontal). This article explores a more general capture method that makes both defocus blur and motion blur nearly invariant to scene depth and in-plane 2D object motion. We formulate the problem as capturing a time-varying light field through a time-varying light field modulator at the lens aperture, and perform 5D (4D light field + 1D time) analysis of all the existing computational cameras for defocus/motion-only deblurring and their hybrids. This leads to a surprising conclusion that focus sweep, previously known as a depth-invariant capture method that moves the plane of focus through a range of scene depth during exposure, is near-optimal both in terms of depth and 2D motion invariance and in terms of high-frequency preservation for certain combinations of depth and motion ranges. Using our prototype camera, we demonstrate joint defocus and motion deblurring for moving scenes with depth variation

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Motion blur in digital images - analys, detection and correction of motion blur in photogrammetry

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming

Coded aperture imaging

This thesis studies the coded aperture camera, a device consisting of a conventional camera with a modified aperture mask, that enables the recovery of both depth map and all-in-focus image from a single 2D input image. Key contributions of this work are the modeling of the statistics of natural images and the design of efficient blur identification methods in a Bayesian framework. Two cases are distinguished: 1) when the aperture can be decomposed in a small set of identical holes, and 2) when the aperture has a more general configuration. In the first case, the formulation of the problem incorporates priors about the statistical variation of the texture to avoid ambiguities in the solution. This allows to bypass the recovery of the sharp image and concentrate only on estimating depth. In the second case, the depth reconstruction is addressed via convolutions with a bank of linear filters. Key advantages over competing methods are the higher numerical stability and the ability to deal with large blur. The all-in-focus image can then be recovered by using a deconvolution step with the estimated depth map. Furthermore, for the purpose of depth estimation alone, the proposed algorithm does not require information about the mask in use. The comparison with existing algorithms in the literature shows that the proposed methods achieve state-of-the-art performance. This solution is also extended for the first time to images affected by both defocus and motion blur and, finally, to video sequences with moving and deformable objects

Blind Image Deconvolution: Model and Computation

Ph.DDOCTOR OF PHILOSOPH

A Framework for Fast Image Deconvolution with Incomplete Observations

In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods, or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast. We iteratively alternate the estimation of the unknown pixels and of the deconvolved image, using, e.g., an FFT-based deconvolution method. This framework is an efficient, high-quality alternative to existing methods of dealing with the image boundaries, such as edge tapering. It can be used with any fast deconvolution method. We give an example in which a state-of-the-art method that assumes periodic boundary conditions is extended, through the use of this framework, to unknown boundary conditions. Furthermore, we propose a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM). We provide a proof of convergence for the resulting algorithm, which can be seen as a "partial" ADMM, in which not all variables are dualized. We report experimental comparisons with other primal-dual methods, where the proposed one performed at the level of the state of the art. Four different kinds of applications were tested in the experiments: deconvolution, deconvolution with inpainting, superresolution, and demosaicing, all with unknown boundaries.Comment: IEEE Trans. Image Process., to be published. 15 pages, 11 figures. MATLAB code available at https://github.com/alfaiate/DeconvolutionIncompleteOb