An effieient algorithm for fractal image coding using kick-out and zero contrast conditions

Title should be: An efficient algorithm for fractal image coding using kick-out and zero contrast conditionsCorrect title: An efficient algorithm for fractal image coding using kick-out and zero contrast conditionsRefereed conference paper2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Gaussian Noise Reduction Method using Adaptive Total Variation; Application to Cone-Beam Computed Tomography Dental Image

The noise generated in the process of obtaining the medical image acts as the element obstructing the image interpretation and diagnosis. To restore the true image from the image polluted from the noise, the total variation optimization algorithm was proposed by the R.O. F (L.Rudin, S Osher, E. Fatemi). This method removes the noise by fitting the balance of the regularity and fidelity. However, the blurring phenomenon of the border area generated in the process of performing the iterative operation cannot be avoided. In this paper, we propose the adaptive total variation method by mapping the control parameter to the proposed transfer function for minimizing boundary error. The proposed transfer function is determined by the noise variance and the local property of the image. The proposed method was applied to 464 tooth images. To evaluate proposed method performance, PSNR which is a indicator of signal and noise’s signal power ratio was used. The experimental results show that the proposed method has better performance than other methods.ope

Fractal Image Compression on MIMD Architectures II: Classification Based Speed-up Methods

Since fractal image compression is computationally very expensive, speed-up techniques are required in addition to parallel processing in order to compress large images in reasonable time. In this paper we discuss parallel fractal image compression algorithms suited for MIMD architectures which employ block classification as speed-up method

Study on High Efficiency Image Coding Using Fractal

長崎大学学位論文　学位記番号:博(海)甲第169号 学位授与年月日:平成12年3月31

Procedimentos para metodo hibrido de compressão de imagens digitais utilizando transformadas Wavelet e codificação fractal

Orientador: Yuzo IanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O principal obstáculo nas implementações da compressão fractal de images é o exaustivo tempo de codificação inerente. O objetivo desta pesquisa é introduzir uma nova aproximação para acelerar a codificação fractal de imagens através da aplicação da TWD e suas propriedades, sem que haja detrimento do PSNR ou da qualidade visual subjetiva. Logo, este trabalho apresenta um novo Codificador Híbrido Fractal-Wavelet, que aplica a compressão fractal acelerada à imagens estáticas decompostas pela transformada wavelet, explorando a correlação direcional das subimagens-wavelet. Este tipo de correlação foi constatada por Shapiro em outro contexto [2]. O esquema proposto promove melhor qualidade visual (compatível com as medidas de PSNR) e uma redução média de cerca de 80% no tempo de codificação-decodificação quando comparado aos resultados da codificação fractal pura para diversas imagens e taxas de bits. Adicionalmente os detalhes da imagem e as características de transmissão progressiva wavelet foram preservados. Nenhum artefato de blocagem, usualmente encontrados em codificadores fractais puros, resultou do processo de compressão híbrido. Os resultados deste trabalho demonstram o potencial da compressão híbrida fractal-wavelet como sendo uma ferramenta poderosa ainda a ser exploradaAbstract: The major drawback in the implementations of the fractal image compression is the exhaustive inherent encoding time. The objective of this research is to introduce a new approach to accelerate the fractal image coding through the application of the DWT and its properties without decrease in the PSNR as well as in the subjective visual quality. Thus, this work presents a New Fast Hybrid Fractal-Wavelet Image Coder that applies the accelerated fractal compression to wavelet transformed images by exploiting the directional correlation of the wavelet subimages. This kind of correlation was noticed by Shapiro in a different context [2]. The proposed scheme promotes better visual quality (compatible to the PSNR measures) and an average reduction of about 80% in encoding-decoding time when compared to the results of the pure accelerated fractal coding for several images and bitrates. Furthermore, the image details and the characteristics of wavelet progressive transmission are maintained; blocking effects, usually found in pure fractal coders, are not introduced. The results of this work demonstrate the potential of the fractal-wavelet hybrid compression as a powerful tool to be further explored.DoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétric

Adaptive Fractal and Wavelet Image Denoising

The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as applied in the spatial, frequency and wavelet domains of the noisy image, the thesis embarks on the endeavor of developing and experimenting with new image denoising methods based on fractal and wavelet transforms. In particular, three new image denoising methods are proposed: context-based wavelet thresholding, predictive fractal image denoising and fractal-wavelet image denoising. The proposed context-based thresholding strategy adopts localized hard and soft thresholding operators which take in consideration the content of an immediate neighborhood of a wavelet coefficient before thresholding it. The two fractal-based predictive schemes are based on a simple yet effective algorithm for estimating the fractal code of the original noise-free image from the noisy one. From this predicted code, one can then reconstruct a fractally denoised estimate of the original image. This fractal-based denoising algorithm can be applied in the pixel and the wavelet domains of the noisy image using standard fractal and fractal-wavelet schemes, respectively. Furthermore, the cycle spinning idea was implemented in order to enhance the quality of the fractally denoised estimates. Experimental results show that the proposed image denoising methods are competitive, or sometimes even compare favorably with the existing image denoising techniques reviewed in the thesis. This work broadens the application scope of fractal transforms, which have been used mainly for image coding and compression purposes