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103
Generalizing the non-local-means to super-resolution reconstruction
- IN IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2009
"... Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inacc ..."
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Cited by 81 (4 self)
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Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.
Super-Resolution From Unregistered and Totally Aliased Signals Using Subspace Methods
, 2007
"... In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the analog-to-digital (A/D) converter, etc. A lowpass filter is usually applied before the sampling operation to avoid aliasing. However, when multiple copies are ..."
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Cited by 28 (8 self)
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In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the analog-to-digital (A/D) converter, etc. A lowpass filter is usually applied before the sampling operation to avoid aliasing. However, when multiple copies are available, it is possible to use the information that is inherently present in the aliasing to reconstruct a higher resolution signal. If the different copies have unknown relative offsets, this is a nonlinear problem in the offsets and the signal coefficients. They are not easily separable in the set of equations describing the super-resolution problem. Thus, we perform joint registration and reconstruction from multiple unregistered sets of samples. We give a mathematical formulation for the problem when there are sets of samples of a signal that is described by expansion coefficients. We prove that the solution of the registration and reconstruction problem is generically unique
Reproducible research in Signal Processing – What, why and how
, 2009
"... Have you ever tried to reproduce the results presented in a research paper? For many of our current publications, this would unfortunately be a challenging task. For a computational algorithm, details such as the exact data set, initialization or termination procedures, and precise parameter values ..."
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Cited by 26 (0 self)
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Have you ever tried to reproduce the results presented in a research paper? For many of our current publications, this would unfortunately be a challenging task. For a computational algorithm, details such as the exact data set, initialization or termination procedures, and precise parameter values are often omitted in the publication for various reasons, such as a lack of space, a lack of self-discipline, or an apparent lack of interest to the readers, to name a few. This makes it difficult, if not impossible, for someone else to obtain
Super Resolution With Probabilistic Motion Estimation
"... Abstract—Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framewo ..."
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Cited by 12 (0 self)
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Abstract—Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that ultimately leads to the same algorithm as in our prior work [11]. The contribution of this paper is two-fold. First, the suggested approach is much simpler and more intuitive, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation. Second, the new approach offers various extensions not covered in our previous work, such as more general re-sampling tasks (e.g., de-interlacing). Index Terms—Deinterlacing, probabilistic motion estimation, super resolution. I.
A nonlinear least square technique for simultaneous image registration and super-resolution
- Image Processing, IEEE Transactions on
"... Abstract—This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in ..."
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Cited by 11 (0 self)
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Abstract—This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algo-rithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs si-multaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images. Index Terms—Image super-resolution (SR), image registration, nonlinear least squares methods. I.
Variational Bayesian Super Resolution
"... Abstract—In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance ofthereconstructedHRimage.Inthispaper ..."
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Cited by 10 (2 self)
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Abstract—In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance ofthereconstructedHRimage.Inthispaper,we proposenovelsuper resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms. Index Terms—Bayesian methods, parameter estimation, super resolution, total variation, variational methods.
Creating Panoramas in mobile phones
- Proceeding of SPIE Electronic Image
"... Image stitching is used to combine several images into one wide-angled mosaic image. Traditionally mosaic images have been constructed from a few separate photographs, but nowadays that video recording has become commonplace even on mobile phones, it is possible to consider also video sequences as a ..."
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Cited by 9 (5 self)
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Image stitching is used to combine several images into one wide-angled mosaic image. Traditionally mosaic images have been constructed from a few separate photographs, but nowadays that video recording has become commonplace even on mobile phones, it is possible to consider also video sequences as a source for mosaic images. However, most stitching methods require vast amounts of computational resources that make them unusable on mobile devices. We present a novel panorama stitching method that is designed to create high-quality image mosaics from both video clips and separate images even on low-resource devices. The software is able to create both 360 degree panoramas and perspective-corrected mosaics. Features of the software include among others: detection of moving objects, inter-frame color balancing and rotation correction. The application selects only the frames of highest quality for the final mosaic image. Low-quality frames are dropped on the fly while recording the frames for the mosaic. The complete software is implemented on Matlab, but also a mobile phone version exists. We present a complete solution from frame acquisition to panorama output with different resource profiles that suit various platforms.
Joint Demosaicing and Super-Resolution Imaging from a Set of Unregistered Aliased Images
"... We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data before interpolation artifacts are introduced ..."
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Cited by 6 (0 self)
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We present a new algorithm that performs demosaicing and super-resolution jointly from a set of raw images sampled with a color filter array. Such a combined approach allows us to compute the alignment parameters between the images on the raw camera data before interpolation artifacts are introduced. After image registration, a high resolution color image is reconstructed at once using the full set of images. For this, we use normalized convolution, an image interpolation method from a nonuniform set of samples. Our algorithm is tested and compared to other approaches in simulations and practical experiments.
