A signal processing approach for enriched region detection in RNA polymerase II ChIP-seq data

International audienc

Image inpainting based on self-organizing maps by using multi-agent implementation

AbstractThe image inpainting is a well-known task of visual editing. However, the efficiency strongly depends on sizes and textural neighborhood of “missing” area. Various methods of image inpainting exist, among which the Kohonen Self-Organizing Map (SOM) network as a mean of unsupervised learning is widely used. The weaknesses of the Kohonen SOM network such as the necessity for tuning of algorithm parameters and the low computational speed caused the application of multi- agent system with a multi-mapping possibility and a parallel processing by the identical agents. During experiments, it was shown that the preliminary image segmentation and the creation of the SOMs for each type of homogeneous textures provide better results in comparison with the classical SOM application. Also the optimal number of inpainting agents was determined. The quality of inpainting was estimated by several metrics, and good results were obtained in complex images

Recursive Non-Local Means Filter for Video Denoising

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality

Adaptive non-local means for multiview image denoising: Searching for the right patches via a statistical approach

ABSTRACT We present an adaptive non-local means (NLM) denoising method for a sequence of images captured by a multiview imaging system, where direct extensions of existing single image NLM methods are incapable of producing good results. Our proposed method consists of three major components: (1) a robust joint-view distance metric to measure the similarity of patches; (2) an adaptive procedure derived from statistical properties of the estimates to determine the optimal number of patches to be used; (3) a new NLM algorithm to denoise using only a set of similar patches. Experimental results show that the proposed method is robust to disparity estimation error, out-performs existing algorithms in multiview settings, and performs competitively in video settings. Index Terms-Non-local means, adaptive filtering, multiview denoising, patch-based denoisin

Discriminant random field and patch-based redundancy analysis for image change detection

International audienceTo develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In con- trast to the usual pixel-wise methods, we propose a patch- based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property is exploited to detect unusual spatio-temporal patterns in the scene. We first define adaptive detectors of changes between two given image patches and combine locally in space and scale such detectors. The resulting score at a given loca- tion is exploited within a discriminant Markov random field (DRF) whose global optimization flags out changes with no optical flow computation. Experimental results on several applications demonstrate that the method performs well at detecting occlusions and meaningful regional changes and is especially robust in the case of low signal-to-noise ratios

Detectors for Super-Resolution & Single-Molecule Fluorescence Microscopies

The resolution of light microscopy was thought to be limited to 250–300 nanometers based on the work of Ernest Abbe. This Abbe diffraction limit was believed to be insurmountable until the invention of Super-resolution microscopic techniques in the late 20th century. These techniques remove this limit and have provided unprecedented detail of cellular structures and dynamics down to several nanometers. An emerging goal in this field is to quantitatively measure individual molecules. Measurement of single-molecule dynamics, such as diffusion coefficients and complex stoichiometries, can be accomplished using fluorescence fluctuation techniques to reveal nanosecond-to-microsecond temporal reactions. These powerful complimentary experimental approaches are made possible by sensitive low-light photodetectors. In this chapter, an overview of the principles of super-resolution and single-molecule microscopies are provided. The different types of photodetectors employed in these techniques are explained. In addition, the advantages and disadvantages for these detectors are discussed, as well as the development of next generation detectors. Finally, example super-resolution and single-molecule cellular studies that take advantage of these detector technologies are presented