From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
We propose a method for partially blind-deconvolution with prior information on the lens characteristics. There is a permanent demand for higher resolution for applications such as tracking, recognition, and identification. Limitations of available methods for practical systems are generally due to computational cost and power. Therefore a computationally efficient method for blind-deconvolution is desirable for practical systems. Total-variation (TV) minimization method proposed by Vogel and Oman is used to recover the image from noisy data and eliminated some of the blurs. Another approach called split augmented Lagrangian shrinkage algorithm uses alternating direction method of multipliers (ADMM) in which an unconstrained optimization problem including ℓ1 data fidelity and a non-smooth regularization term are solved. Although successful, the excessive computational requirements present a challenge for practical usage of these methods. Here, we propose a parametric blind-deconvolution method with prior knowledge on the point spread function (PSF) of the camera lens. We model the PSF of the circular optics as Jinc-squared function and determine the best PSF by solving optimization problem containing TV-norm along with Wavelet-sparsity objectives using an ADMM based algorithm. We use a convolutional model and work in Fourier domain for efficient implementation, and avoid circular effects by extending the unknown image region. First, we show that PSF function of the lenses can be modeled with Jinc function in experimental data. Next, we point out that our algorithm improves resolution of the image and compared to classical blind-deconvolution methods while remaining feasible in terms of computation time.
Melih Bastopcu, Alper Güngör, and H. Emre Güven, "An efficient algorithm for model based blind deconvolution," Proc. SPIE 10222, Computational Imaging II, 102220B (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 09, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2263281.
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