A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gibbs Random Fields, Cooccurrences, and Texture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Super-Resolution Reconstruction of Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative MPEG Super-Resolution with an Outer Approximation of Framewise Quantization Constraint
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Bayesian super-resolution approach to demosaicing of blurred images
EURASIP Journal on Applied Signal Processing
A fast algorithm for image super-resolution from blurred observations
EURASIP Journal on Applied Signal Processing
Adaptive outlier rejection in image super-resolution
EURASIP Journal on Applied Signal Processing
Video-to-video dynamic super-resolution for grayscale and color sequences
EURASIP Journal on Applied Signal Processing
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Simultaneous multichannel image restoration and estimation of the regularization parameters
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Bayesian and regularization methods for hyperparameter estimation in image restoration
IEEE Transactions on Image Processing
Superresolution restoration of an image sequence: adaptive filtering approach
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Parameter estimation in Bayesian high-resolution image reconstruction with multisensors
IEEE Transactions on Image Processing
Super-resolution reconstruction of compressed video using transform-domain statistics
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
An Improved Observation Model for Super-Resolution Under Affine Motion
IEEE Transactions on Image Processing
Efficient Huber-Markov Edge-Preserving Image Restoration
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Regularized constrained total least squares image restoration
IEEE Transactions on Image Processing
Super-resolution still and video reconstruction from MPEG-coded video
IEEE Transactions on Circuits and Systems for Video Technology
Robust color image superresolution: an adaptive M-estimation framework
Journal on Image and Video Processing - Color in Image and Video Processing
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Automatic, robust global motion estimation using clustering
Image Communication
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Recently, there has been a great deal of work developing super-resolution reconstruction (SRR) algorithms. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation, therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore, the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper first comprehensively reviews the SRR algorithms in this last decade and addresses their shortcomings, and latter proposes a novel robust SRR algorithm that can be applied on several noise models. The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. For removing outliers in the data, the Lorentzian error norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image. Moreover, Tikhonov regularization and Lorentzian-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norms for several noise models such as noiseless, additive white Gaussian noise (AWGN), poisson noise, salt and pepper noise, and speckle noise.