Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring

Ph.DDOCTOR OF PHILOSOPH

Simultaneous Stereo Video Deblurring and Scene Flow Estimation

Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods. We evaluate our method extensively on two available datasets and achieve significant improvement in flow estimation and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 201

Enhancing Video Deblurring using Efficient Fourier Aggregation

Video Deblurring is a process of removing blur from all the video frames and achieving the required level of smoothness. Numerous recent approaches attempt to remove image blur due to camera shake,either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem.An efficient video deblurring system to handle the blurs due to shaky camera and complex motion blurs due to moving objects has been proposed.The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method�s rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently.The proposed system has effectively deblurred the video and results showed that the reconstructed video is sharper and less noisy than the original ones.The proposed Fourier Burst Accumulation algorithm produced similar or better results than the state-of-the-art multi-image deconvolution while being significantly faster and with lower memory footprint.The method is robust to moving objects as it acquired the consistent registration scheme

Automatic isolation of blurred images from UAV image sequences

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 filtering process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. A “shaking table” was used to create images with known blur during a series of laboratory tests. This platform can be moved in one direction by a mathematical function controlled by a defined frequency and amplitude. The shaking table was used to displace a Nikon D80 digital SLR camera with a user defined frequency and amplitude. The actual camera displacement was measured accurately and exposures were synchronized, which provided the opportunity to acquire images with a known blur effect. Acquired images were processed digitally to determine a quantifiable measure of image blur, which has been created by the actual shaking table function. Once determined for a sequence of images, a user defined threshold can be used to differentiate between “blurred” and "acceptable" images. A subsequent step is to establish the effect that blurred images have upon the accuracy of subsequent measurements. Both of these aspects will be discussed in this paper and future work identified

Influence of blur on feature matching and a geometric approach for photogrammetric deblurring

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by a UAV, which have a high ground resolution and good spectral and radiometric resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost efficient 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 aim of this research is to develop a blur correction method to deblur UAV 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, which is advantageous. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images may limit the application. Deblurring images needs to focus on geometric correct deblurring to assure geometric correct measurements

Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms

Computational Photography, Deblurring, Low-level Vision, Datasets and EvaluationNumerous learning-based approaches to single image deblurring for camera and object motion blurs have recently been proposed. To generalize such approaches to real-world blurs, large datasets of real blurred images and their ground truth sharp images are essential. However, there are still no such datasets, thus all the existing approaches resort to synthetic ones, which leads to the failure of deblurring real-world images. In this work, we present a large-scale dataset of real-world blurred images and their corresponding sharp images captured in low-light environments for learning and benchmarking single image deblurring methods. To collect our dataset, we build an image acquisition system to simultaneously capture a geometrically aligned pair of blurred and sharp images, and develop a post-processing method to further align images geometrically and photometrically. We analyze the effect of our post-processing step, and the performance of existing learning-based deblurring methods. Our analysis shows that our dataset significantly improves deblurring quality for real-world low-light images.Y1. Introduction 1 2. Related Work 2 3. Image Acquisition System and Process 3 3.1 Image Acquisition System 3 3.2 Image Acquisition Process 4 4. Post-Processing 5 4.1 Downsampling & Denoising 6 4.2 Geometric Alignment 6 4.3 Photometric Alignment 8 5. Experiments 8 5.1 Analysis of RealBlur Dataset 9 5.2 Benchmark 12 6. Conclusion 19 7. Appendix 20 8. References 24 9. 요약문 28MasterdCollectio