3D Reconstruction using convolution smooth method

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.

Robust low rank and sparse decomposition from blurred video frames

<div>Recently, the low-rank and sparse decomposition</div><div>problem has attracted attention in several applications, especially surveillance videos. Due to the physical limitations in acquisition systems, measured frames are blurred by a low-pass filter.</div><div>In this article, we aim to decompose blurred videos’ frames</div><div>into low-rank and sparse components, in order to extract the</div><div>background. Unlike conventional methods, we simultaneously take into account the blurring effect, as well as the missing data. Our simulation results confirmed the advantage of this approach in extracting low-rank components in surveillance videos.</div>

Robust low rank and sparse decomposition from blurred video frames

<div>Recently, the low-rank and sparse decomposition</div><div>problem has attracted attention in several applications, especially surveillance videos. Due to the physical limitations in acquisition systems, measured frames are blurred by a low-pass filter.</div><div>In this article, we aim to decompose blurred videos’ frames</div><div>into low-rank and sparse components, in order to extract the</div><div>background. Unlike conventional methods, we simultaneously take into account the blurring effect, as well as the missing data. Our simulation results confirmed the advantage of this approach in extracting low-rank components in surveillance videos.</div>

Robust low rank and sparse decomposition from blurred video frames

<div>Recently, the low-rank and sparse decomposition</div><div>problem has attracted attention in several applications, especially surveillance videos. Due to the physical limitations in acquisition systems, measured frames are blurred by a low-pass filter.</div><div>In this article, we aim to decompose blurred videos’ frames</div><div>into low-rank and sparse components, in order to extract the</div><div>background. Unlike conventional methods, we simultaneously take into account the blurring effect, as well as the missing data. Our simulation results confirmed the advantage of this approach in extracting low-rank components in surveillance videos.</div>

A Study on Digital Image Restoration Filters

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.

Single image haze removal using multiple scattering model for road scenes

Haze is one of the sources cause image degradation. Haze affects contrast and saturation of not only for the real world image, but also the road scenes. Most haze removal algorithms use an atmospheric scattering model for removing the effect of haze. Most of haze removal algorithms are based on the single scattering model which does not consider the blur in the haze image. In this paper, a novel haze removal algorithm using a multiple scattering model with deconvolution is proposed. The proposed algorithm considers blurring effect in the haze image. Down sampling of the haze image is also used for estimating the atmospheric light efficiently. The synthetic road scenes with and without haze are used to evaluate the performance of the proposed method. Experimental result demonstrates that the proposed algorithm performs better for restoring images affected by haze both qualitatively and quantitatively.

Origin and diversification of Xanthomonas citri subsp. citri pathotypes revealed by inclusive phylogenomic, dating, and biogeographic analyses

Background Xanthomonas citri subsp. citri pathotypes cause bacterial citrus canker, being responsible for severe agricultural losses worldwide. The A pathotype has a broad host spectrum, while A* and Aw are more restricted both in hosts and in geography. Two previous phylogenomic studies led to contrasting well-supported clades for sequenced genomes of these pathotypes. No extensive biogeographical or divergence dating analytic approaches have been so far applied to available genomes. Results Based on a larger sampling of genomes than in previous studies (including six new genomes sequenced by our group, adding to a total of 95 genomes), phylogenomic analyses resulted in different resolutions, though overall indicating that A + AW is the most likely true clade. Our results suggest the high degree of recombination at some branches and the fast diversification of lineages are probable causes for this phylogenetic blurring effect. One of the genomes analyzed, X. campestris pv. durantae, was shown to be an A* strain; this strain has been reported to infect a plant of the family Verbenaceae, though there are no reports of any X. citri subsp. citri pathotypes infecting any plant outside the Citrus genus. Host reconstruction indicated the pathotype ancestor likely had plant hosts in the family Fabaceae, implying an ancient jump to the current Rutaceae hosts. Extensive dating analyses indicated that the origin of X. citri subsp. citri occurred more recently than the main phylogenetic splits of Citrus plants, suggesting dispersion rather than host-directed vicariance as the main driver of geographic expansion. An analysis of 120 pathogenic-related genes revealed pathotype-associated patterns of presence/absence. Conclusions Our results provide novel insights into the evolutionary history of X. citri subsp. citri as well as a sound phylogenetic foundation for future evolutionary and genomic studies of its pathotypes.

Digital Image Restoration in Matlab: A Case Study on Inverse and Wiener Filtering

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

The Fundamentals of Medical Image Restoration

This chapter simply encapsulates the basics of image restoration, various noise models, and degradation model including some blur and image restoration filters. The mining of high resolution information from the low-resolution images is a very vital task in several applications of digital image processing. In recent times, a lot of research work has been carried out in this field in order to improve the resolution of real medical images especially when the given images are corrupted with some kind of noise. The displayed images are the result of the various stages that might cause imperfections in the digital images, for instance the so-called imaging and capturing process can itself degrade the original scene. The imperfections present in the image need to be studied and analyzed if the noise present in the images is not modelled properly. There are different types of degradations which are considered such as noise, geometrical degradations, imperfections (due to improper illumination and color), and blur. Blurring in the images is generally caused by the relative motion between the camera and the original object being captured or due to poor focusing of an optical system. In the production of aerial photographs for remote sensing purposes, blurs are introduced by the atmospheric turbulence, aberrations in the optical system, and relative motion between the camera and the ground. Apart from the blurring effect, noise also creates imperfections in the images that corrupt the images under analysis. The noise may be introduced by several factors (e.g., medium, recording or capturing system, or by the quantization process). Due to this noise or blur present in the images, resolution needs to be improved and the image is to be restored from the geometrically warped, blurred, and noisy images.