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Projeto Descrição

GREYCstoration is an image regularization
algorithm that processes an image by locally
removing small variations of pixel intensities
while preserving significant global image
features, such as sharp edges and corners.
The most direct application is denoising. By
extension, it can also be used to inpaint or
resize images. It is based on methods using
nonlinear multi-valued diffusion PDEs (Partial
Differential Equations) for image
regularization. This kind of method generally
outperforms basic image filtering techniques
(such as convolution, median filtering, etc.)
classically encountered in painting programs.
Other image denoising plugins are available,
but are not free, and the corresponding
algorithms are kept secret.

System Requirements

System requirement is not defined
Information regarding Project Releases and Project Resources. Note that the information here is a quote from Freecode.com page, and the downloads themselves may not be hosted on OSDN.

2006-03-27 16:36
2.3

O algoritmo de regularização foi melhorado e agora realiza três vezes mais rápido que versões anteriores. A borda-filtro de realce foi adicionado, permitindo que mais agudamente imagens restauradas. Alguns erros com nomes de arquivos contendo espaços foram corrigidos.
Tags: Major feature enhancements
The regularization algorithm has been improved and
now performs three times faster than previous
versions. A edge-enhancement filter was added,
allowing more sharply restored images. Some bugs
with filenames containing spaces have been
corrected.

2006-02-11 09:57
2.2

O algoritmo está agora apto para dar um critério de qualidade denoising (o "Peak Signal to Noise Ratio ') após denoising uma imagem que tenha sido artificialmente corrompido pelo usuário. Isso permite uma comparação com outros métodos numéricos denoising existentes. A interface foi modificado para mostrar o original, barulhento e imagens restauradas quando o usuário tem a imagem corrompida com ruído artificial.
Tags: Minor feature enhancements
The algorithm is now ables to give a denoising quality criterion (the 'Peak Signal to Noise Ratio') after denoising an image that has been
artificially corrupted by the user. This allows
a numerical comparison with other existing denoising methods. The interface was modified to show the original, noisy, and
restored images when the user has corrupted the image with artificial noise.

2006-02-07 02:32
2.1

Tags: Initial freshmeat announcement

Project Resources