Resolution enhancement of color video sequences

Shah, N.; Zakhor, Avideh

doi:10.1109/83.766865

Cited by 82 publications

(33 citation statements)

References 13 publications

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“…This matrix's only task is normalization of the weighted average that took place on the RHS. As this matrix is invertible, the new solution is obtained by (10) When using the fixed-point method, as we did above, every appearance of the unknown in the equation is assigned with an iteration number. Among the many possible assignments, one should seek one that satisfies two important conditions: i) The computation of from should be easy; and ii) The obtained iterative formula should lead to convergence.…”

Section: B Derivation Of the Nlm Filtermentioning

confidence: 99%

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

Protter

Elad

Takeda

et al. 2009

IEEE Trans. on Image Process.

640

407

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Abstract-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.

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Section: B Derivation Of the Nlm Filtermentioning

confidence: 99%

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

Protter

Elad

Takeda

et al. 2009

IEEE Trans. on Image Process.

640

407

View full text Add to dashboard Cite

show abstract

“…To date, there is very little work addressing the problem of color SR. The typical solution involves applying monochromatic SR algorithms to each of the color channels independently [11], [12], while using the color information to improve the accuracy of motion estimation. Another approach is transforming the problem to a different color space, where chrominance layers are separated from luminance, and SR is applied only to the luminance channel [7].…”

Section: A Super-resolutionmentioning

confidence: 99%

Multiframe demosaicing and super-resolution of color images

Farsiu

Elad

Milanfar

2006

IEEE Trans. on Image Process.

307

244

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In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a MAP estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.

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“…To date, there is very little work addressing the problem of color SR. The typical solution involves applying monochromatic SR algorithms to each of the color channels independently [20,21], while using the color information to improve the accuracy of motion estimation. Another approach is transforming the problem to a different color space, where chrominance layers are separated from luminance, and SR is applied only to the luminance channel [3].…”

Section: Color and Srmentioning

confidence: 99%

<title>Dynamic demosaicing and color superresolution of video sequences</title>

et al. 2004

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In the last two decades a variety of super-resolution (SR) methods have been proposed. These methods usually address the problem of fusing a set of monochromatic images to produce a single monochromatic image with higher spatial resolution. In this paper we address the dynamic and color SR problems of reconstructing a high-quality set of colored super-resolved images from low-quality mosaiced frames. Our approach includes a hybrid method for simultaneous SR and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter. Experimental results on both simulated and real data are supplied, demonstrating the presented algorithm, and its strength.

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Resolution enhancement of color video sequences

Cited by 82 publications

References 13 publications

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

Multiframe demosaicing and super-resolution of color images

<title>Dynamic demosaicing and color superresolution of video sequences</title>

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