Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

Department of Computer Science, The Technion-Israel Institute of Technology, Haifa, Israel.
IEEE Transactions on Image Processing (Impact Factor: 3.63). 02/2009; 18(1):36-51. DOI: 10.1109/TIP.2008.2008067
Source: PubMed


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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    • "Reconstruction stage combines information from the series of registered images into a single image with more definition and quality (Kang & Chaudhuri, 2003; Yang & Huang, 2010). Buades et al. (2005) proposed the nonlocal means (NLM) denoising algorithm and Protter et al. (2009) adapted the method for SR purposes, showing a very robust behaviour against inaccuracies in registration and motion tracking, as well as in electron microscopy images (Binev et al., 2012; Mevenkamp et al., 2014). NLM assumes that image content is likely to repeat itself within some neighbourhood; therefore the algorithm calculates a weighted averaging on those pixels in the same patch (search window) whose intensity distributions are close to each other, in terms of the Euclidean distance. "
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    ABSTRACT: Super-resolution (SR) software-based techniques aim at generating a final image by combining several noisy frames with lower resolution from the same scene. A comparative study on high-resolution high-angle annular dark field images of InAs/GaAs QDs has been carried out in order to evaluate the performance of the SR technique. The obtained SR images present enhanced resolution and higher signal-to-noise (SNR) ratio and sharpness regarding the experimental images. In addition, SR is also applied in the field of strain analysis using digital image processing applications such as geometrical phase analysis and peak pairs analysis. The precision of the strain mappings can be improved when SR methodologies are applied to experimental images.
    Journal of Microscopy 10/2015; DOI:10.1111/jmi.12341 · 2.33 Impact Factor
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    • "For a given unknown image patch, many similar patches which are either close or far to this patch might be found. This type of nonlocal similarity has been effectively used in image de-noising [20] [21] [22] [23], de-blurring [24] [25] and super resolution [26] [27]. NARM aims to model a given pixel as the linear combination of its nonlocal neighbouring pixels. "
    02/2015; 7(3):38-44. DOI:10.5815/ijigsp.2015.03.06
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    • "We use a non-local data-fidelity term instead of a non-local regularization. This type of approach has been used in a super-resolution context by Protter et al. [12] and d'Angelo and Vandergheynst [13]. They use the normalized weights issued from the NL-means to define a non-local datafidelity term. "
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    ABSTRACT: We derive a denoising method based on an adaptive regularization of the non-local means. The NL-means reduce noise by using the redundancy in natural images. They compute a weighted average of pixels whose surroundings are close. This method performs well but it suffers from residual noise on singular structures. We use the weights computed in the NL-means as a measure of performance of the denoising process. These weights balance the data-fidelity term in an adapted ROF model, in order to locally perform adaptive TV regularization. Besides, this model can be adapted to different noise statistics and a fast resolution can be computed in the general case of the exponential family. We adapt this model to video denoising by using spatio-temporal patches. Compared to spatial patches, they offer better temporal stability, while the adaptive TV regularization corrects the residual noise observed around moving structures.
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