Eigenvalues and condition numbers of random matrices
SIAM Journal on Matrix Analysis and Applications
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Entropy Approach in the Analysis of Anisotropy of Digital Images
Open Systems & Information Dynamics
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
No-reference quality metrics for eye fundus imaging
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Fast parameter sensitivity analysis of PDE-based image processing methods
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Adaptive regularization-based space-time super-resolution reconstruction
Image Communication
A no-reference metric for evaluating the quality of motion deblurring
ACM Transactions on Graphics (TOG)
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Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.