High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution

The challenge of image impulse noise removal is to restore spatial details from damaged pixels using remaining ones in random locations. Most existing methods use all uncontaminated pixels within a local window to estimate the centred noisy one via a statistic way. These kinds of methods have two defects. First, all noisy pixels are treated as independent individuals and estimated by their neighbours one by one, with the correlation between their true values ignored. Second, the image structure as a natural feature is usually ignored. This study proposes a new denoising framework, in which all noisy pixels are jointly restored via non-uniform sampling and supervised piecewise autoregressive modelling based super-resolution. In this method, the noisy pixels are jointly estimated in groups through solving a well-designed optimisation problem, in which image structure feature is considered as an important constraint. Another contribution is that piecewise autoregressive model is not simply adopted but carefully designed so that all noise-free pixels can be used to supervise the model training and optimisation problem solving for higher accuracy. The experimental results demonstrate that the proposed method exhibits good denoising performance in a large noise density range (10-90%).