A Unified Approach to Superresolution and Multichannel Blind Deconvolution

@article{roubek2007AUA,
  title={A Unified Approach to Superresolution and Multichannel Blind Deconvolution},
  author={Filip {\vS}roubek and Gabriel Crist{\'o}bal and Jan Flusser},
  journal={IEEE Transactions on Image Processing},
  year={2007},
  volume={16},
  pages={2322-2332}
}
This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles… 

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References

SHOWING 1-10 OF 36 REFERENCES
Multichannel blind deconvolution of spatially misaligned images
TLDR
This work developed an alternating minimization scheme based on a maximum a posteriori estimation with a priori distribution of blurs derived from the multichannel framework and a priora distribution of original images defined by the variational integral to recover the blurs and the original image from channels severely corrupted by noise.
Total variation blind deconvolution
TLDR
A blind deconvolution algorithm based on the total variational (TV) minimization method proposed is presented, and it is remarked that psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.
Fast and robust multiframe super resolution
TLDR
This paper proposes an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models and demonstrates its superiority to other super-resolution methods.
Multichannel blind iterative image restoration
TLDR
A novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included is proposed and performs well even on very noisy images and does not require an exact estimation of mask orders.
Multichannel blind image deconvolution using the Bussgang algorithm: spatial and multiresolution approaches
This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial
Blind superresolution from undersampled blurred measurements
  • A. Yagle
  • Mathematics
    SPIE Optics + Photonics
  • 2003
TLDR
This work shows that irregular sampling allows reconstruction of an MXM high-resolution image from L2 low-resolution images blurred with an LXL blurring function can be achieved with as few as L2 + (M/L)2 pixels in each low- resolution image.
Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement
TLDR
This work estimates PSF parameters for this ill-posed class of inverse problem from raw data, along with the regularization parameters required to stabilize the solution, using the generalized cross-validation method (GCV).
Blind image restoration by anisotropic regularization
This paper presents anisotropic regularization techniques to exploit the piecewise smoothness of the image and the point spread function (PSF) in order to mitigate the severe lack of information
A general approach to blind image super-resolution using a PDE framework
Blind super-resolution (BSR) is one of the challenges in the super-resolution image reconstruction area. In this paper, we propose a general approach, which is based on a partial differential
Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images
TLDR
This work proposes two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images based on a Bayesian formulation that is implemented via the expectation maximization algorithm and a maximum a posteriori formulation.
...
...