Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

A well-posedness framework for inpainting based on coherence transport

Image inpainting is the process of touching-up damaged or unwanted portions of a picture and is an important task in image processing. For this purpose Bornemann and März [J. Math. Imaging Vis. , 28 (2007), pp. 259– 278] introduced a very efficient method called Image Inpainting Based on Coherence Transport which fills the missing region by advecting the image information along integral curves of a coherence vector field from the boundary towards the interior of the hole. The mathematical model behind this method is a first-order functional advection PDE posed on a compact domain with all inflow boundary. We show that this problem is well-posed under certain conditions

A texture based approach to reconstruction of archaeological finds

Reconstruction of archaeological finds from fragments, is a tedious task requiring many hours of work from the archaeologists and restoration personnel. In this paper we present a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The confidence of this process is also defined. Feature values are derived from these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. The optimization of total affinity gives the best assembly of the piece. Experimental results are presented on real and artificial data

Image Inpainting Methods: Digital Image Reconstruction and Restoration

This report investigates the digital image processing of image inpainting methods, particularly for digital image reconstruction and restoration through the two computational tools grouped into: [1] MatLAB/Image Segmenter and [2] Anaconda/OpenCV/Python. The use cases explored in the project involve image reconstruction and restoration of celestial imageries for means of clear demonstration and subject-matter consistency but can extend to more artistic purposes involving the removal of unwanted objects within the backgrounds or foregrounds of images that can be “erased” or “hidden” by being replaced by neighboring pixels of similar characteristics for image reconstruction and the removal of damaged parts observed in old photographs damaged by noises, dark streaks, faded or scratched edges, folds, physio-chemical alterations, ink blotches, or technological obscurities (such as lens flare, lens aberrations, or crop marks) for image restoration. The celestial images used for the purposes of this project are taken from the public collection of NASA’s James Webb and Hubble Telescope image archives

An Examplar Based Video Inpainting using Dictionary Based Method

Inpainting is a skill of rebuilding lost or selected part from the image based on relatedor available information. Reconstruction of missing parts in videos is used extensively nowadays. A method for video inpainting usingexamplar-based inpainting is introduced in the system. The examplar based inpainting samples and copies best matching texture patches using texture synthesis. Matching patches are extracted from the known part of the frames from the video. Input frames are extracted and inpainted using examplar based method. For that dictionary is maintained which consists of legal patches. The input picture isinpainted several times with different parameters. Then it is combined and details are recovered to get the final inpainted video

Mathematical Approaches to Digital Image Inpainting

Image inpainting process is used to restore the damaged image or missing parts of an image. This technique is used in some applications, such as removal of text in images and photo restoration. There are different types of methods used in image inpainting, such as non-inear partial differential equations(PDEs), wavelet transformation and framelet transformation. We studied the usage of the current image inpainting methods and solved the Poisson equation using a five-point stencil method. We used a modified five-point stencil method to solve the same equation. It gave better results than the standard five-point stencil method. Using modified five-point stencil method results as the initial condition, we solved the iterative linear and non-linear diffusion PDE. We considered different types of diffusion conductivity and compared their results. When compared with PSNR values, the iterative linear diffusion PDE method gave the best results where as constant diffusion conductivity PDE gave the worst result. Furthermore, inverse diffusion conductivity PDE had given better results than that of the constant diffusion PDE. However, it was worse than the Gaussian and Lorentz diffusion conductivity PDE. Gaussian and Lorentz diffusion conductivity iterative linear PDE had given a better result for image inpainting. When we use any inpainting technique, we cannot restore the original image. We studied the relationship between the error of the image inpainting and the inpainted domain. Error is proportional to the value of the Greens function. There are two types of methods to find the Greens function. The first method is solving a Poisson equation for a different shape of domain, such as a circle, ellipse, triangle and rectangle. If the inpainting domain has a different shape, then it is difficult to find the error. We used the conformal mapping method to find the error. We also developed a formula for transformation from any polygon to the unit circle. Moreover, we applied the Schwarz Christoffel transformation to transform from the upper half plane to any polygon