CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.04 - April
Issue No.04 - April (2012 vol.34)
W. T. Freeman , Massachusetts Inst. of Technol., Cambridge, MA, USA
The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.
maximum likelihood estimation, deconvolution, image restoration, image texture, iterative methods, reference distribution, image restoration, gradient distribution matching, blurry image, noisy image, MAP estimator, maximum a priori estimator, posterior probability, sparse gradient image prior, piecewise smooth image, image texture, visual realism, deconvolution method, iterative distribution reweighting method, Image reconstruction, Image restoration, Noise, Deconvolution, Kernel, Cost function, Gaussian distribution, image denoising., Nonblind deconvolution, image prior, image deblurring
W. T. Freeman, "Image Restoration by Matching Gradient Distributions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 4, pp. 683-694, April 2012, doi:10.1109/TPAMI.2011.166