Digital Image Restoration
Digital Image Processing in Remote Sensing
Digital Image Processing in Remote Sensing
On the Hierarchical Bayesian Approach to Image Restoration: Applications to Astronomical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal discrete periodic Radon transform: part II: applications
Signal Processing
Multiresolution circular harmonic decomposition
IEEE Transactions on Signal Processing
Bussgang-zero crossing equalization: an integrated HOS-SOS approach
IEEE Transactions on Signal Processing
Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms
IEEE Transactions on Image Processing
Wavelet domain image restoration with adaptive edge-preserving regularization
IEEE Transactions on Image Processing
Hierarchical Bayesian image restoration from partially known blurs
IEEE Transactions on Image Processing
Blind identification of multichannel FIR blurs and perfect image restoration
IEEE Transactions on Image Processing
A multiresolution approach for texture synthesis using the circular harmonic functions
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Stochastic nonlinear image restoration using the wavelet transform
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Blurred image restoration: A fast method of finding the motion length and angle
Digital Signal Processing
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This work addresses the problem of blind image deblurring, that is, of recovering an original image observed through one or more unknown linear channels and corrupted by additive noise. We resort to an iterative algorithm, belonging to the class of Bussgang algorithms, based on alternating a linear and a nonlinear image estimation stage. In detail, we investigate the design of a novel nonlinear processing acting on the Radon transform of the image edges. This choice is motivated by the fact that the Radon transform of the image edges well describes the structural image features and the effect of blur, thus simplifying the nonlinearity design. The effect of the nonlinear processing is to thin the blurred image edges and to drive the overall blind restoration algorithm to a sharp, focused image. The performance of the algorithm is assessed by experimental results pertaining to restoration of blurred natural images.