IEEE Trans Image Process - Image enhancement using the hypothesis selection filter: theory and application to JPEG decoding.

Tópicos

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Resumo

We introduce the hypothesis selection filter (HSF) as a new approach for image quality enhancement. We assume that a set of filters has been selected a priori to improve the quality of a distorted image containing regions with different characteristics. At each pixel, HSF uses a locally computed feature vector to predict the relative performance of the filters in estimating the corresponding pixel intensity in the original undistorted image. The prediction result then determines the proportion of each filter used to obtain the final processed output. In this way, the HSF serves as a framework for combining the outputs of a number of different user selected filters, each best suited for a different region of an image. We formulate our scheme in a probabilistic framework where the HSF output is obtained as the Bayesian minimum mean square error estimate of the original image. Maximum likelihood estimates of the model parameters are determined from an offline fully unsupervised training procedure that is derived from the expectation-maximization algorithm. To illustrate how to apply the HSF and to demonstrate its potential, we apply our scheme as a post-processing step to improve the decoding quality of JPEG-encoded document images. The scheme consistently improves the quality of the decoded image over a variety of image content with different characteristics. We show that our scheme results in quantitative improvements over several other state-of-the-art JPEG decoding methods.

Resumo Limpo

introduc hypothesi select filter hsf new approach imag qualiti enhanc assum set filter select priori improv qualiti distort imag contain region differ characterist pixel hsf use local comput featur vector predict relat perform filter estim correspond pixel intens origin undistort imag predict result determin proport filter use obtain final process output way hsf serv framework combin output number differ user select filter best suit differ region imag formul scheme probabilist framework hsf output obtain bayesian minimum mean squar error estim origin imag maximum likelihood estim model paramet determin offlin fulli unsupervis train procedur deriv expectationmaxim algorithm illustr appli hsf demonstr potenti appli scheme postprocess step improv decod qualiti jpegencod document imag scheme consist improv qualiti decod imag varieti imag content differ characterist show scheme result quantit improv sever stateoftheart jpeg decod method

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