We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing.

An approach to multi-channel blind deconvolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps (during the learning process) the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments verify a proper blind deconvolution of convolution mixtures of sources.

Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even wi...

In the following paper we investigate two algorithms for blind signal deconvolution that has been proposed in the literature. We derive a clear interpretation of the information theoretic objective function in terms of signal processing and show that only one is appropriate to solve the deconvolution problem, while the other will only work if the unknown filter is constrained to be minimum phas...

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We derive near optimal performance guarantees for subsampled blind deconvolution. Blind deconvolution is an ill-posed bilinear inverse problem and additional subsampling makes the problem even more challenging. Sparsity and spectral flatness priors on unknown signals are introduced to overcome these difficulties. While being crucial for deriving desired near optimal performance guarantees, unli...

In this paper, we discuss blind deconvolution of dynamical systems, described by the state space model. First we formulate blind deconvolution problem in the framework of the state space model. The blind deconvolution is fulfilled in two stages: internal representation and signal separation. We employ two different learning strategies for training the parameters in the two stages. A sparse repr...

In the following paper we investigate two algorithms for blind signal deconvolution that has been proposed in the literature. We derive a clear interpretation of the information theoretic objective function in terms of signal processing and show that only one is appropriate to solve the deconvolution problem, while the other will only work if the unknown filter is constrained to be minimum phas...

Image deconvolution is a challenging ill-posed problem when only partial information of the blur kernel is available. Certain regularization on sharp images has to be imposed to constrain the estimation of true images during the blind deconvolution process. Based on the observation that an image of sharp edges tends to minimize the ratio between the `1 norm and the `2 norm of its wavelet frame ...

In this paper we propose a blind deconvolution algorithm for wide field fluorescence microscopy. The 3D PSF is modeled after a parametrized pupil function. The PSF parameters are estimated jointly with the object in a maximum a posteriori framework. We illustrate the performances of our algorithm on experimental data and show significant resolution improvement notably along the depth. Quantitat...