MOTIVATION: Determining the methylation state of regions with high copy numbers is challenging for second-generation sequencing, because the read length is insufficient to map reads uniquely, especially when repetitive regions are long and nearly identical to each other. Single-molecule real-time (SMRT) sequencing is a promising method for observing such regions, because it is not vulnerable to GC bias, it produces long read lengths, and its kinetic information is sensitive to DNA modifications. RESULTS: We propose a novel linear-time algorithm that combines the kinetic information for neighboring CpG sites and increases the confidence in identifying the methylation states of those sites. Using a practical read coverage of ∼30-fold from an inbred strain medaka (Oryzias latipes), we observed that both the sensitivity and precision of our method on individual CpG sites were ∼93.7%. We also observed a high correlation coefficient (R = 0.884) between our method and bisulfite sequencing, and for 92.0% of CpG sites, methylation levels ranging over [0,1] were in concordance within an acceptable difference 0.25. Using this method, we characterized the landscape of the methylation status of repetitive elements, such as LINEs, in the human genome, thereby revealing the strong correlation between CpG density and hypomethylation and detecting hypomethylation hot spots of LTRs and LINEs. We uncovered the methylation states for nearly identical active transposons, two novel LINE insertions of identity ∼99% and length 6050 base pairs (bp) in the human genome, and 16 Tol2 elements of identity >99.8% and length 4682 bp in the medaka genome. AVAILABILITY AND IMPLEMENTATION: AgIn (Aggregate on Intervals) is available at: https://github.com/hacone/AgIn CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Although spatial deblurring is relatively well understood by assuming that the blur kernel is shift invariant, motion blur is not so when we attempt to deconvolve on a frame-by-frame basis: this is because, in general, videos include complex, multilayer transitions. Indeed, we face an exceedingly difficult problem in motion deblurring of a single frame when the scene contains motion occlusions. Instead of deblurring video frames individually, a fully 3-D deblurring method is proposed in this paper to reduce motion blur from a single motion-blurred video to produce a high-resolution video in both space and time. Unlike other existing approaches, the proposed deblurring kernel is free from knowledge of the local motions. Most importantly, due to its inherent locally adaptive nature, the 3-D deblurring is capable of automatically deblurring the portions of the sequence, which are motion blurred, without segmentation and without adversely affecting the rest of the spatiotemporal domain, where such blur is not present. Our method is a two-step approach; first we upscale the input video in space and time without explicit estimates of local motions, and then perform 3-D deblurring to obtain the restored sequence.

In this paper, we propose a kernel backprojection method for computed tomography. The classical backprojection method estimates an unknown pixel value by the summation of the projection values with linear weights, while our kernel backprojection is a generalized version of the classic approach, in which we compute the weights from a kernel (weight) function. The generalization reveals that the performance of the backprojection operation strongly depends on the choice of the kernel, and a good choice of the kernels effectively suppresses both noise and streak artifacts while preserving major structures of the unknown phantom. The proposed method is a two-step procedure where we first compute a preliminary estimate of the phantom (a “pilot”), from which we compute the kernel weights. From these kernel weights we then reestimate the phantom, arriving at a much improved result. The experimental results show that our approach significantly enhances the backprojection operation not only numerically but also visually.

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.

The need for precise (subpixel accuracy) motion estimates in conventional super-resolution has limited its applicability to only video sequences with relatively simple motions such as global translational or affine displacements. In this paper, we introduce a novel framework for adaptive enhancement and spatiotemporal upscaling of videos containing complex activities without explicit need for accurate motion estimation. Our approach is based on multidimensional kernel regression, where each pixel in the video sequence is approximated with a 3-D local (Taylor) series, capturing the essential local behavior of its spatiotemporal neighborhood. The coefficients of this series are estimated by solving a local weighted least-squares problem, where the weights are a function of the 3-D space-time orientation in the neighborhood. As this framework is fundamentally based upon the comparison of neighboring pixels in both space and time, it implicitly contains information about the local motion of the pixels across time, therefore rendering unnecessary an explicit computation of motions of modest size. The proposed approach not only significantly widens the applicability of super-resolution methods to a broad variety of video sequences containing complex motions, but also yields improved overall performance. Using several examples, we illustrate that the developed algorithm has super-resolution capabilities that provide improved optical resolution in the output, while being able to work on general input video with essentially arbitrary motion.

In this paper, we extend a (2-D) data-adaptive steering kernel regression framework for image processing to a (3-D) spatio-temporal framework for processing video. In particular, we propose a motion- assisted steering kernel (MASK) suitable for interpolating video data spatially, temporally, or spatio-temporally, and for video noise reduction. We present an algorithm for multi-frame interpolation and reconstruction of video data, and present several simulation results on synthetic and real video data. Comparisons between single-frame and multi-frame kernel regression and with other methods demonstrate the effectiveness of our approach.

Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method.

In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.

We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected samples. The filters derived from this class are locally adapted kernels which take into account both the local density of the available samples, and the actual values of these samples. As such, they are automatically steered and adapted to both the given sampling "geometry", and the samples' "radiometry". As the framework we proposed does not rely upon specific assumptions about noise or sampling distributions, it is applicable to a wide class of problems including efficient image upscaling, high quality reconstruction of an image from as little as 15% of its (irregularly sampled) pixels, super-resolution from noisy and under-determined data sets, state of the art denoising of images corrupted by Gaussian and other noise, effective removal of compression artifacts; and more.