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By considering an adaptive parameter within the usual divergence process, we retain the powerful denoising capability of anisotropic diffusion PDE without any oscillating artifacts. To improve the anisotropic diffusion based schemes and to avoid the well-known drawbacks such as edge blurring and ‘staircasing’ artifacts, in this paper, we consider a class of weighted anisotropic diffusion partial differential equations (PDEs). The proposed framework can ultimately assist in identification of promising rangeland areas, the identified areas subsequently explored as per necessary follow-up actions/procedures.Īnisotropic diffusion is a key concept in digital image denoising and restoration. This integration results in identifying promising cultivable regions for the long-term productivity of perennial pasture grasses in the Kingdom of Saudi Arabia. In this paper, we propose a data-driven framework and present simulated results mapped to real data that show how predictive data mining, geographical information system and expert system can be integrated. Therefore, the exploration of novel and proven IT techniques and methodologies are needed to address this complex problem. The traditional methods to achieve these objectives are expensive, complex and time-consuming. To meet the increasing dietary (cereal, meat, milk, etc.), needs of people and the fodder needs of livestock require identification of additional cultivation regions and correspondingly suitable crop/grass varieties. The population of Saudi Arabia is increasing so is the demand for food however, the arable land that can support this demand is decreasing rapidly. The experiments carried out show the proposed method's efficiency. The proposed method is capable of reconstructing the curvatures of the patient's internal structures without using slices that are close to one another. In the second step of the proposed method, the created virtual slices will be used together with the real slices images, in the reconstruction of the structure in three dimensions, mapped onto the exam. The virtual slices contain all similarity between the intercaleted slices and, when there aren't similarities between real slices, the virtual slices will contain indefinite portions. To do it, considering the high similarity between two consecutive real slice, the first step of the proposed method is to create the virtual slices. Inspired by this idea, this article proposes an interpolation method for the filling in of the empty regions between the CT slices. The PDE's have been used, in the image-processing context to fill in the damaged regions in a digital 2D image. The inpainting is carried out via non-linear partial differential equations (PDE).
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Motivated towards the goal of reaching an improved balance between quantity of slices and visualization quality, this research work presents a digital inpainting technique of 3D interpolation for CT slices used in the visualization of human body structures. To obtain a high quality image it is necessary to obtain slices which are close to one another.
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The visualization of a computerized tomographic (TC) exam in 3D increases the quality of the medical diagnosis and, consequently, the success probability in the treatment. The experimental results show the effective performance of the combination of these two procedures in restoring scratched photos, disocclusion (or removal of entire objects from the image) in vision analysis and text removal from images. This combination permits the simultaneous use of filling-in and differentiated smoothing of different regions of an image. Besides smoothing, the approach here presented permits the transportation of available information from the outside towards the inside of the inpainting domain. Inside the inpainting domain, the smoothing is carried out by the Mean Curvature Flow, while the smoothing of the outside of the inpainting domain is carried out in a way as to encourage smoothing within a region and discourage smoothing across boundaries. The denoising is performed by the smoothing equation working inside and outside of the inpainting domain but in completely different ways. Our method simultaneously fills in missing, corrupted, or undesirable information while it removes noise. In this paper, we present a new approach for image restoration.
![inpaint photo restoration inpaint photo restoration](https://www.rosl.org.uk/images/Blog_images/20191011_123721.jpg)
Inpainting and denoising are two important tasks in the field of image processing with broad applications in image and vision analysis.
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