42 research outputs found
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๏ฟฝs 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
Coded exposure photography: motion deblurring using fluttered shutter
In a conventional single-exposure photograph, moving objects or moving cameras cause motion blur. The exposure time defines a temporal box filter that smears the moving object across the image by convolution. This box filter destroys important high-frequency spatial details so that deblurring via deconvolution becomes an illposed problem. Rather than leaving the shutter open for the entire exposure duration, we โflutter โ the cameraโs shutter open and closed during the chosen exposure time with a binary pseudo-random sequence. The flutter changes the box filter to a broad-band filter that preserves high-frequency spatial details in the blurred image and the corresponding deconvolution becomes a well-posed problem. We demonstrate that manually-specified point spread functions are sufficient for several challenging cases of motionblur removal including extremely large motions, textured backgrounds and partial occluders. ACM Transactions o Graphics (TOG
Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes
abstract: Digital imaging and image processing technologies have revolutionized the way in which
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
์์ง์ด๋ ๋จ์ผ ์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ 3์ฐจ์ ๋ณต์๊ณผ ๋๋ธ๋ฌ๋ง, ์ดํด์๋ ๋ณต์์ ๋์์ ์ํ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2013. 8. ์ด๊ฒฝ๋ฌด.์์ ๊ธฐ๋ฐ 3์ฐจ์ ๋ณต์์ ์ปดํจํฐ ๋น์ ์ ๊ธฐ๋ณธ์ ์ธ ์ฐ๊ตฌ ์ฃผ์ ๊ฐ์ด๋ฐ ํ๋๋ก ์ต๊ทผ ๋ช ๋
๊ฐ ๋ง์ ๋ฐ์ ์ด ์์ด์๋ค. ํนํ ์๋ ๋ก๋ด์ ์ํ ๋ค๋น๊ฒ์ด์
๋ฐ ํด๋ ๊ธฐ๊ธฐ๋ฅผ ์ด์ฉํ ์ฆ๊ฐ ํ์ค ๋ฑ์ ๋๋ฆฌ ํ์ฉ๋ ์ ์๋ ๋จ์ผ ์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ 3์ฐจ์ ๋ณต์ ๊ธฐ๋ฒ์ ๋ณต์์ ์ ํ๋, ๋ณต์ ๊ฐ๋ฅ ๋ฒ์ ๋ฐ ์ฒ๋ฆฌ ์๋ ์ธก๋ฉด์์ ๋ง์ ์ค์ฉ ๊ฐ๋ฅ์ฑ์ ๋ณด์ฌ์ฃผ๊ณ ์๋ค. ๊ทธ๋ฌ๋ ๊ทธ ์ฑ๋ฅ์ ์ฌ์ ํ ์กฐ์ฌ์ค๋ ์ดฌ์๋ ๋์ ํ์ง์ ์
๋ ฅ ์์์ ๋ํด์๋ง ์ํ๋๊ณ ์๋ค. ์์ง์ด๋ ๋จ์ผ ์นด๋ฉ๋ผ๋ฅผ ์ด์ฉํ 3์ฐจ์ ๋ณต์์ ์ค์ ๋์ ํ๊ฒฝ์์๋ ์
๋ ฅ ์์์ด ํ์ ์ก์์ด๋ ์์ง์์ ์ํ ๋ฒ์ง ๋ฑ์ ์ํ์ฌ ์์๋ ์ ์๊ณ , ์์์ ํด์๋ ๋ํ ์ ํํ ์นด๋ฉ๋ผ ์์น ์ธ์ ๋ฐ 3์ฐจ์ ๋ณต์์ ์ํด์๋ ์ถฉ๋ถํ ๋์ง ์์ ์ ์๋ค. ๋ง์ ์ฐ๊ตฌ์์ ๊ณ ์ฑ๋ฅ ์์ ํ์ง ํฅ์ ๊ธฐ๋ฒ๋ค์ด ์ ์๋์ด ์์ง๋ง ์ด๋ค์ ์ผ๋ฐ์ ์ผ๋ก ๋์ ๊ณ์ฐ ๋น์ฉ์ ํ์๋ก ํ๊ธฐ ๋๋ฌธ์ ์ค์๊ฐ ๋์ ๋ฅ๋ ฅ์ด ์ค์ํ ๋จ์ผ ์นด๋ฉ๋ผ ๊ธฐ๋ฐ 3์ฐจ์ ๋ณต์์ ์ฌ์ฉ๋๊ธฐ์๋ ๋ถ์ ํฉํ๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋ณด๋ค ์ ํํ๊ณ ์์ ๋ ๋ณต์์ ์ํ์ฌ ์์ ๊ฐ์ ์ด ๊ฒฐํฉ๋ ์๋ก์ด ๋จ์ผ ์นด๋ฉ๋ผ ๊ธฐ๋ฐ 3์ฐจ์ ๋ณต์ ๊ธฐ๋ฒ์ ๋ค๋ฃฌ๋ค. ์ด๋ฅผ ์ํ์ฌ ์์ ํ์ง์ด ์ ํ๋๋ ์ค์ํ ๋ ์์ธ์ธ ์์ง์์ ์ํ ์์ ๋ฒ์ง๊ณผ ๋ฎ์ ํด์๋ ๋ฌธ์ ๊ฐ ๊ฐ๊ฐ ์ ๊ธฐ๋ฐ ๋ณต์ ๋ฐ ์กฐ๋ฐ ๋ณต์ ๊ธฐ๋ฒ๋ค๊ณผ ๊ฒฐํฉ๋๋ค. ์์ ํ์ง ์ ํ๋ฅผ ํฌํจํ ์์ ํ๋ ๊ณผ์ ์ ์นด๋ฉ๋ผ ๋ฐ ์ฅ๋ฉด์ 3์ฐจ์ ๊ธฐํ ๊ตฌ์กฐ์ ๊ด์ธก๋ ์์ ์ฌ์ด์ ๊ด๊ณ๋ฅผ ์ด์ฉํ์ฌ ๋ชจ๋ธ๋ง ํ ์ ์๊ณ , ์ด๋ฌํ ์์ ํ์ง ์ ํ ๊ณผ์ ์ ๊ณ ๋ คํจ์ผ๋ก์จ ์ ํํ 3์ฐจ์ ๋ณต์์ ํ๋ ๊ฒ์ด ๊ฐ๋ฅํด์ง๋ค. ๋ํ, ์์ ๋ฒ์ง ์ ๊ฑฐ๋ฅผ ์ํ ๋ฒ์ง ์ปค๋ ๋๋ ์์์ ์ดํด์๋ ๋ณต์์ ์ํ ํ์ ๋์ ์ ๋ณด ๋ฑ์ด 3์ฐจ์ ๋ณต์ ๊ณผ์ ๊ณผ ๋์์ ์ป์ด์ง๋๊ฒ์ด ๊ฐ๋ฅํ์ฌ, ์์ ๊ฐ์ ์ด ๋ณด๋ค ๊ฐํธํ๊ณ ๋น ๋ฅด๊ฒ ์ํ๋ ์ ์๋ค. ์ ์๋๋ ๊ธฐ๋ฒ์ 3์ฐจ์ ๋ณต์๊ณผ ์์ ๊ฐ์ ๋ฌธ์ ๋ฅผ ๋์์ ํด๊ฒฐํจ์ผ๋ก์จ ๊ฐ๊ฐ์ ๊ฒฐ๊ณผ๊ฐ ์ํธ ๋ณด์์ ์ผ๋ก ํฅ์๋๋ค๋ ์ ์์ ๊ทธ ์ฅ์ ์ ๊ฐ์ง๊ณ ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์คํ์ ํ๊ฐ๋ฅผ ํตํ์ฌ ์ ์๋๋ 3์ฐจ์ ๋ณต์ ๋ฐ ์์ ๊ฐ์ ์ ํจ๊ณผ์ฑ์ ์
์ฆํ๋๋ก ํ๋ค.Vision-based 3D reconstruction is one of the fundamental problems in computer vision, and it has been researched intensively significantly in the last decades. In particular, 3D reconstruction using a single camera, which has a wide range of applications such as autonomous robot navigation and augmented reality, shows great possibilities in its reconstruction accuracy, scale of reconstruction coverage, and computational efficiency. However, until recently, the performances of most algorithms have been tested only with carefully recorded, high quality input sequences. In practical situations, input images for 3D reconstruction can be severely distorted due to various factors such as pixel noise and motion blur, and the resolution of images may not be high enough to achieve accurate camera localization and scene reconstruction results. Although various high-performance image enhancement methods have been proposed in many studies, the high computational costs of those methods prevent applying them to the 3D reconstruction systems where the real-time capability is an important issue.
In this dissertation, novel single camera-based 3D reconstruction methods that are combined with image enhancement methods is studied to improve the accuracy and reliability of 3D reconstruction. To this end, two critical image degradations, motion blur and low image resolution, are addressed for both sparse reconstruction and dense 3D reconstruction systems, and novel integrated enhancement methods for those degradations are presented. Using the relationship between the observed images and 3D geometry of the camera and scenes, the image formation process including image degradations is modeled by the camera and scene geometry. Then, by taking the image degradation factors in consideration, accurate 3D reconstruction then is achieved. Furthermore, the information required for image enhancement, such as blur kernels for deblurring and pixel correspondences for super-resolution, is simultaneously obtained while reconstructing 3D scene, and this makes the image enhancement much simpler and faster. The proposed methods have an advantage that the results of 3D reconstruction and image enhancement are improved by each other with the simultaneous solution of these problems. Experimental evaluations demonstrate the effectiveness of the proposed 3D reconstruction and image enhancement methods.1. Introduction
2. Sparse 3D Reconstruction and Image Deblurring
3. Sparse 3D Reconstruction and Image Super-Resolution
4. Dense 3D Reconstruction and Image Deblurring
5. Dense 3D Reconstruction and Image Super-Resolution
6. Dense 3D Reconstruction, Image Deblurring, and Super-Resolution
7. ConclusionDocto
Video Depth-From-Defocus
Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available. Existing computational methods to capture RGB and depth either purposefully modify the optics (coded aperture, light-field imaging), or employ active RGB-D cameras. Since these methods are less practical for users with normal cameras, we present an algorithm to capture all-in-focus RGB-D video of dynamic scenes with an unmodified commodity video camera. Our algorithm turns the often unwanted defocus blur into a valuable signal. The input to our method is a video in which the focus plane is continuously moving back and forth during capture, and thus defocus blur is provoked and strongly visible. This can be achieved by manually turning the focus ring of the lens during recording. The core algorithmic ingredient is a new video-based depth-from-defocus algorithm that computes space-time-coherent depth maps, deblurred all-in-focus video, and the focus distance for each frame. We extensively evaluate our approach, and show that it enables compelling video post-processing effects, such as different types of refocusing
Recommended from our members
Panoramic Video Stitching
Digital camera and smartphone technologies have made high quality images and video pervasive and abundant. Combining or stitching collections of images from a variety of viewpoints into an extended panoramic image is a common and popular function for such devices. Extending this functionality to video however, poses many new challenges due to the demand for both spatial and temporal continuity. Multi-view video stitching (also called panoramic video stitching) is an emerging, common research area in computer vision, image/video processing and computer graphics and has wide applications in virtual reality, virtual tourism, surveillance, and human computer interaction. In this thesis, I will explore the technical and practical problems in the complete process of stitching a high-resolution multiview video into a high-resolution panoramic video. The challenges addressed include video stabilization, efficient multi-view video alignment and panoramic video stitching, color correction, and blurred frame detection and repair.
Specifically, I propose a continuity aware Kalman filtering scheme for rotation angles for video stabilization and jitter removal. For efficient stitching of long, high-resolution panoramic videos, I propose constrained and multigrid SIFT matching schemes, concatenated image projection and warping and min-space feathering. These three approaches together can greatly reduce the computational time and memory requirement in panoramic video stitching, which makes it feasible to stitch high-resolution (e.g., 1920x1080 pixels) and long panoramic video sequences using standard workstations.
Color correction is the emphasis of my research. On this topic I first performed a systematic survey and performance evaluation of nine state of the art color correction approaches in the context of two-view image stitching. My evaluation work not only gives useful insights and conclusions about the relative performance of these approaches, but also points out the remaining challenges and possible directions for future color correction research. Based on the conclusions from this evaluation work, I proposed a hybrid and scalable color correction approach for general n-view image stitching, and designed a two-view video color correction approach for panoramic video stitching.
For blurred frame detection and repair, I have completed preliminary work on image partial blur detection and classification, in which I proposed a SVM-based blur block classifier using improved and new local blur features. Then, based on partial blur classification results, I designed a statistical thresholding scheme for blurred frame identification. For the detected blurred frames, I repaired them using polynomial data fitting from neighboring unblurred frames.
Many of the techniques and ideas in this thesis are novel and general solutions to the technical or practical problems in panoramic video stitching. At the end of this thesis, I conclude the contributions made by this thesis to the research and popularization of panoramic video stitching, and describe those open research issues