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56
Image alignment and stitching: a tutorial
, 2006
"... This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panora ..."
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Cited by 115 (2 self)
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This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce
Robust Super-Resolution
- in In Proc. of the IEEE Workshop on Applications of Computer Vision
, 2001
"... A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion erros, inaccurate blur models, noise, moving objects, motion blur etc. This tobusiness is needed since super-resolution methods are very sensitive to such ..."
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Cited by 92 (0 self)
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A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion erros, inaccurate blur models, noise, moving objects, motion blur etc. This tobusiness is needed since super-resolution methods are very sensitive to such errors.
Image Mosaicing and Superresolution
, 2004
"... The thesis investigates the problem of how information contained in multiple, overlapping images of the same scene may be combined to produce images of superior quality. This area, generically titled frame fusion, offers the possibility of reducing noise, extending the field of view, removal of movi ..."
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Cited by 66 (4 self)
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The thesis investigates the problem of how information contained in multiple, overlapping images of the same scene may be combined to produce images of superior quality. This area, generically titled frame fusion, offers the possibility of reducing noise, extending the field of view, removal of moving objects, removing blur, increasing spatial resolution and improving dynamic range. As such, this research has many applications in fields as diverse as forensic image restoration, computer generated special effects, video image compression, and digital video editing. An essential enabling step prior to performing frame fusion is image registration, by which an accurate estimate of the point-to-point mapping between views is computed. A robust and efficient algorithm is described to automatically register multiple images using only information contained within the images themselves. The accuracy of this method, and the statistical assumptions upon which it relies, are investigated empirically. Two forms of frame-fusion are investigated. The first is image mosaicing, which is the alignment of multiple images into a single composition representing part of a 3D scene.
Space-time super-resolution
- PAMI
, 2005
"... We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By âtemporal super-resolutionâ we mean recoverin ..."
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Cited by 65 (2 self)
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We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By âtemporal super-resolutionâ we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else observed incorrectly) in any of the input sequences, even if these are played in âslow-motionâ. The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual tradeoffs in time and space, and to new video applications. These include: (i) treatment of spatial artifacts (e.g., motionblur) by increasing the temporal resolution, and (ii) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: How many video cameras are needed to obtain increased resolution? What is the upper bound on resolution improvement via super-resolution? What is the optimal camera configuration for various scenarios? What is the temporal analogue to the spatial âringingâ effect?
Bayesian Image Super-resolution
- Advances in Neural Information Processing Systems
, 2003
"... The extraction of a single high-quality image from a set of lowresolution images is an important problem which arises in elds such as remote sensing, surveillance, medical imaging and the extraction of still images from video. Typical approaches are based on the use of cross-correlation to regi ..."
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Cited by 62 (1 self)
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The extraction of a single high-quality image from a set of lowresolution images is an important problem which arises in elds such as remote sensing, surveillance, medical imaging and the extraction of still images from video. Typical approaches are based on the use of cross-correlation to register the images followed by the inversion of the transformation from the unknown high resolution image to the observed low resolution images, using regularization to resolve the ill-posed nature of the inversion process. In this paper we develop a Bayesian treatment of the super-resolution problem in which the likelihood function for the image registration parameters is based on a marginalization over the unknown high-resolution image. This approach allows us to estimate the unknown point spread function, and is rendered tractable through the introduction of a Gaussian process prior over images. Results indicate a signi cant improvement over techniques based on MAP (maximum a-posteriori) point optimization of the high resolution image and associated registration parameters.
Wavelet algorithms for high-resolution image reconstruction
- SIAM Journal on Scientific Computing
"... Abstract. High-resolution image reconstruction refers to the reconstruction of high-resolution images from multiple low-resolution, shifted, degraded samples of a true image. In this paper, we analyze this problem from the wavelet point of view. By expressing the true image as a function in L(R2), w ..."
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Cited by 55 (29 self)
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Abstract. High-resolution image reconstruction refers to the reconstruction of high-resolution images from multiple low-resolution, shifted, degraded samples of a true image. In this paper, we analyze this problem from the wavelet point of view. By expressing the true image as a function in L(R2), we derive iterative algorithms which recover the function completely in the L sense from the given low-resolution functions. These algorithms decompose the function obtained from the previous iteration into different frequency components in the wavelet transform domain and add them into the new iterate to improve the approximation. We apply wavelet (packet) thresholding methods to denoise the function obtained in the previous step before adding it into the new iterate. Our numerical results show that the reconstructed images from our wavelet algorithms are better than that from the Tikhonov least-squares approach. Extension to super-resolution image reconstruction, where some of the low-resolution images are missing, is also considered.
Increasing space-time resolution in video
- In ECCV
, 2002
"... Abstract. We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple lowresolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By “temporal super-resolution ” we mean rec ..."
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Cited by 44 (1 self)
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Abstract. We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple lowresolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By “temporal super-resolution ” we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else observed incorrectly) in any of the input sequences, even if these are played in “slow-motion”. The spatial and temporal dimensions are very different in nature, yet are inter-related. This leads to interesting visual tradeoffs in time and space, and to new video applications. These include: (i) treatment of spatial artifacts (e.g., motion-blur) by increasing the temporal resolution, and (ii) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. Keywords. Super-resolution, space-time analysis. 1
Robust Shift and Add Approach to Super-Resolution
, 2003
"... resolution enhancement. These methods, which have a wide range of complexity, memory and time requirements, are usually very sensitive to their assumed model of data and noise, often limiting their utility. Di#erent implementations of the non-iterative Shift and Add concept have been proposed as ver ..."
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Cited by 27 (8 self)
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resolution enhancement. These methods, which have a wide range of complexity, memory and time requirements, are usually very sensitive to their assumed model of data and noise, often limiting their utility. Di#erent implementations of the non-iterative Shift and Add concept have been proposed as very fast and e#ective superresolution algorithms. The paper of Elad & Hel-Or 2001 provided an adequate mathematical justification for the Shift and Add method for the simple case of an additive Gaussian noise model. In this paper we prove that additive Gaussian distribution is not a proper model for super-resolution noise. Specifically, we show that L p norm minimization (1 2) results in a pixelwise weighted mean algorithm which requires the least possible amount of computation time and memory and produces a maximum likelihood solution. We also justify the use of a robust prior information term based on bilateral filter idea. Finally, for the underdetermined case, where the number of non-redundant low-resolution frames are less than square of the resolution enhancement factor, we propose a method for detection and removal of outlier pixels. Our experiments using commercial digital cameras show that our proposed super-resolution method provides significant improvements in both accuracy and e#ciency.
