42 research outputs found

    Enhancing Video Deblurring using Efficient Fourier Aggregation

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    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

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    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 โ€flutter โ€ the cameraโ€™s 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

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    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

    ์›€์ง์ด๋Š” ๋‹จ์ผ ์นด๋ฉ”๋ผ๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๋ณต์›๊ณผ ๋””๋ธ”๋Ÿฌ๋ง, ์ดˆํ•ด์ƒ๋„ ๋ณต์›์˜ ๋™์‹œ์  ์ˆ˜ํ–‰ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2013. 8. ์ด๊ฒฝ๋ฌด.์˜์ƒ ๊ธฐ๋ฐ˜ 3์ฐจ์› ๋ณต์›์€ ์ปดํ“จํ„ฐ ๋น„์ „์˜ ๊ธฐ๋ณธ์ ์ธ ์—ฐ๊ตฌ ์ฃผ์ œ ๊ฐ€์šด๋ฐ ํ•˜๋‚˜๋กœ ์ตœ๊ทผ ๋ช‡ ๋…„๊ฐ„ ๋งŽ์€ ๋ฐœ์ „์ด ์žˆ์–ด์™”๋‹ค. ํŠนํžˆ ์ž๋™ ๋กœ๋ด‡์„ ์œ„ํ•œ ๋„ค๋น„๊ฒŒ์ด์…˜ ๋ฐ ํœด๋Œ€ ๊ธฐ๊ธฐ๋ฅผ ์ด์šฉํ•œ ์ฆ๊ฐ• ํ˜„์‹ค ๋“ฑ์— ๋„๋ฆฌ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๋‹จ์ผ ์นด๋ฉ”๋ผ๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๋ณต์› ๊ธฐ๋ฒ•์€ ๋ณต์›์˜ ์ •ํ™•๋„, ๋ณต์› ๊ฐ€๋Šฅ ๋ฒ”์œ„ ๋ฐ ์ฒ˜๋ฆฌ ์†๋„ ์ธก๋ฉด์—์„œ ๋งŽ์€ ์‹ค์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ทธ ์„ฑ๋Šฅ์€ ์—ฌ์ „ํžˆ ์กฐ์‹ฌ์Šค๋ ˆ ์ดฌ์˜๋œ ๋†’์€ ํ’ˆ์งˆ์˜ ์ž…๋ ฅ ์˜์ƒ์— ๋Œ€ํ•ด์„œ๋งŒ ์‹œํ—˜๋˜๊ณ  ์žˆ๋‹ค. ์›€์ง์ด๋Š” ๋‹จ์ผ ์นด๋ฉ”๋ผ๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๋ณต์›์˜ ์‹ค์ œ ๋™์ž‘ ํ™˜๊ฒฝ์—์„œ๋Š” ์ž…๋ ฅ ์˜์ƒ์ด ํ™”์†Œ ์žก์Œ์ด๋‚˜ ์›€์ง์ž„์— ์˜ํ•œ ๋ฒˆ์ง ๋“ฑ์— ์˜ํ•˜์—ฌ ์†์ƒ๋  ์ˆ˜ ์žˆ๊ณ , ์˜์ƒ์˜ ํ•ด์ƒ๋„ ๋˜ํ•œ ์ •ํ™•ํ•œ ์นด๋ฉ”๋ผ ์œ„์น˜ ์ธ์‹ ๋ฐ 3์ฐจ์› ๋ณต์›์„ ์œ„ํ•ด์„œ๋Š” ์ถฉ๋ถ„ํžˆ ๋†’์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค. ๋งŽ์€ ์—ฐ๊ตฌ์—์„œ ๊ณ ์„ฑ๋Šฅ ์˜์ƒ ํ™”์งˆ ํ–ฅ์ƒ ๊ธฐ๋ฒ•๋“ค์ด ์ œ์•ˆ๋˜์–ด ์™”์ง€๋งŒ ์ด๋“ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋†’์€ ๊ณ„์‚ฐ ๋น„์šฉ์„ ํ•„์š”๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์‹œ๊ฐ„ ๋™์ž‘ ๋Šฅ๋ ฅ์ด ์ค‘์š”ํ•œ ๋‹จ์ผ ์นด๋ฉ”๋ผ ๊ธฐ๋ฐ˜ 3์ฐจ์› ๋ณต์›์— ์‚ฌ์šฉ๋˜๊ธฐ์—๋Š” ๋ถ€์ ํ•ฉํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ณ  ์•ˆ์ •๋œ ๋ณต์›์„ ์œ„ํ•˜์—ฌ ์˜์ƒ ๊ฐœ์„ ์ด ๊ฒฐํ•ฉ๋œ ์ƒˆ๋กœ์šด ๋‹จ์ผ ์นด๋ฉ”๋ผ ๊ธฐ๋ฐ˜ 3์ฐจ์› ๋ณต์› ๊ธฐ๋ฒ•์„ ๋‹ค๋ฃฌ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ์˜์ƒ ํ’ˆ์งˆ์ด ์ €ํ•˜๋˜๋Š” ์ค‘์š”ํ•œ ๋‘ ์š”์ธ์ธ ์›€์ง์ž„์— ์˜ํ•œ ์˜์ƒ ๋ฒˆ์ง๊ณผ ๋‚ฎ์€ ํ•ด์ƒ๋„ ๋ฌธ์ œ๊ฐ€ ๊ฐ๊ฐ ์  ๊ธฐ๋ฐ˜ ๋ณต์› ๋ฐ ์กฐ๋ฐ€ ๋ณต์› ๊ธฐ๋ฒ•๋“ค๊ณผ ๊ฒฐํ•ฉ๋œ๋‹ค. ์˜์ƒ ํ’ˆ์งˆ ์ €ํ•˜๋ฅผ ํฌํ•จํ•œ ์˜์ƒ ํš๋“ ๊ณผ์ •์€ ์นด๋ฉ”๋ผ ๋ฐ ์žฅ๋ฉด์˜ 3์ฐจ์› ๊ธฐํ•˜ ๊ตฌ์กฐ์™€ ๊ด€์ธก๋œ ์˜์ƒ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ธ๋ง ํ•  ์ˆ˜ ์žˆ๊ณ , ์ด๋Ÿฌํ•œ ์˜์ƒ ํ’ˆ์งˆ ์ €ํ•˜ ๊ณผ์ •์„ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ์ •ํ™•ํ•œ 3์ฐจ์› ๋ณต์›์„ ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ๋˜ํ•œ, ์˜์ƒ ๋ฒˆ์ง ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๋ฒˆ์ง ์ปค๋„ ๋˜๋Š” ์˜์ƒ์˜ ์ดˆํ•ด์ƒ๋„ ๋ณต์›์„ ์œ„ํ•œ ํ™”์†Œ ๋Œ€์‘ ์ •๋ณด ๋“ฑ์ด 3์ฐจ์› ๋ณต์› ๊ณผ์ •๊ณผ ๋™์‹œ์— ์–ป์–ด์ง€๋Š”๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜์—ฌ, ์˜์ƒ ๊ฐœ์„ ์ด ๋ณด๋‹ค ๊ฐ„ํŽธํ•˜๊ณ  ๋น ๋ฅด๊ฒŒ ์ˆ˜ํ–‰๋  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋˜๋Š” ๊ธฐ๋ฒ•์€ 3์ฐจ์› ๋ณต์›๊ณผ ์˜์ƒ ๊ฐœ์„  ๋ฌธ์ œ๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ๊ฐ๊ฐ์˜ ๊ฒฐ๊ณผ๊ฐ€ ์ƒํ˜ธ ๋ณด์™„์ ์œผ๋กœ ํ–ฅ์ƒ๋œ๋‹ค๋Š” ์ ์—์„œ ๊ทธ ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹คํ—˜์  ํ‰๊ฐ€๋ฅผ ํ†ตํ•˜์—ฌ ์ œ์•ˆ๋˜๋Š” 3์ฐจ์› ๋ณต์› ๋ฐ ์˜์ƒ ๊ฐœ์„ ์˜ ํšจ๊ณผ์„ฑ์„ ์ž…์ฆํ•˜๋„๋ก ํ•œ๋‹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

    Image Motion Analysis using Inertial Sensors

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    Video Depth-From-Defocus

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    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
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