Investigation on basic methods for digital image restoration

El siguiente documento, tras la presentación del trabajo y las motivaciones que llevan a escribirlo, va a tratar sobre el tratamiento digital de imagen y sus aplicaciones, focalizándose en la degradación y restauración de dichas imágenes. En primer lugar se explicará el filtrado espacial y frecuencial, para familiarizar al lector con dichos términos ya que serán de gran importancia en el desarrollo posterior. Una vez sentadas las bases se tratará el ruido, tanto con ruido Gaussiano como ruido impulsivo. A continuación las imágenes degradadas anteriormente serán restauradas mediante filtro paso bajo y filtro de mediana, respectivamente. Luego se habla del motion blur con ruido y sin ruido, usándose el filtro inverso y filtro de Wiener para su restauración. En el ultimo apartado se desarrollan sus conclusioneEl següent document, després de la presentació del treball i les motivacions que porten a escriure-ho, tractarà sobre el tractament digital d'imatge i les seues aplicacions, focalitzant-se en la degradació i restauració de les dites imatges. En primer lloc s'explicarà el filtrat espacial i freqüencial, per a familiaritzar el lector amb els dits termes ja que seran de gran importància en el desenrotllament posterior. Una vegada assentades les bases es tractarà el soroll, tant el soroll Gaussià com el soroll impulsiu. A continuació les imatges degradades anteriorment seran restaurades per mitjà del filtre pas davall i filtre de mitjana, respectivament. Després es parla del motion blur amb soroll i sense soroll, usant-se el filtre invers i filtre de Wiener per a la seua restauració. En l'ultim apartat es desenrotllen les seues conclusions.In the following text, after the presentation of the paper and the motivations that lead to write it, it is going to talk about digital image processing and its applications, focusing on the degradation and restoration of said images. In first place it explains the frequential and spatial filtering, to help the reader to get along with the terminology that will be very important it the following development. Once all the basics are learned, the noise will be treathed, both Gaussian and impulsive. Next, the previously degradated images will be restored using low pass filter and median filter, in that order. Then it talks about motion blur with and without noise, using inverse filter and Wiener filter for its restoration. In the last part the conclusion will be developedBallester Romaniv, O. (2015). Investigation on basic methods for digital image restoration. Universitat Politècnica de València. http://hdl.handle.net/10251/57222TFG

Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution

Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a reference frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma de Mallorca, Spai

Learning Moore-Penrose based residuals for robust non-blind image deconvolution

This work was supported by grants P20_00286 and B-TIC-324-UGR20 funded by Consejería de Universidad, Investigación e Innovación ( Junta de Andalucía ) and by “ ERDF A way of making Europe”. Funding for open access charge: Universidad de Granada / CBUA.This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.Junta de Andalucía P20_00286, B-TIC-324-UGR20ERDF A way of making EuropeUniversidad de Granada / CBU

Image Restoration using RBF Neural Network and Filling-In Technique

Image restoration is known as enhancement and recovery of images. Personal pictures captured by varied digital cameras will simply be manipulated by a range of dedicated image process algorithms .The aim of this paper is to implement a model of neural network with Filling-in technique to resolve the problem of image restoration, which is retrieving the original image degraded by invariant blur. The algorithm is proposed in this paper implements a general RBF neural network model with Probabilistic approach which differentiates the pixels of image according to their level of corruption and employees different ways to correct it. Less corrupted are corrected by remaining part of pixel using Filling-in technique, while others are corrected by using RBF neural network image restoration resulting in better signal to blur noise and better visual quality. DOI: 10.17762/ijritcc2321-8169.15026