A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis
Linear and Nonlinear Image Deblurring: A Documented Study
SIAM Journal on Numerical Analysis
Pattern Recognition Letters
Calibrating spectral images using penalized likelihood
Real-Time Imaging - Special issue on spectral imaging
Iterative evaluation of the regularization parameter in regularized image restoration
Journal of Visual Communication and Image Representation
Maximum likelihood blur identification and image restoration usingthe EM algorithm
IEEE Transactions on Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
Expectation-maximization algorithms, null spaces, and MAP image restoration
IEEE Transactions on Image Processing
Total Variation for Image Restoration with Smooth Area Protection
Journal of Signal Processing Systems
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In this paper, we present an adaptive gradient based method to restore images degraded by the effects of both noise and blur. The approach combines two penalty functions. The first derivative of the Canny operator is employed as a roughness penalty function to improve the high frequency information content of the image and a smoothing penalty term is used to remove noise. An adaptive algorithm is used to select the roughness and smoothing control parameters. We evaluate our approach using the Richardson-Lucy EM algorithm as a benchmark. The results highlight some of the difficulties in restoring blurred images that are subject to noise and show that in this case an algorithm that uses a combined penalty function is able to produce better quality results.