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Efficient Fourier-Wavelet Super-Resolution
"... Abstract—Super-resolution (SR) is the process of combining multiple aliased low-quality images to produce a high-resolution high-quality image. Aside from registration and fusion of low-resolution images, a key process in SR is the restoration and denoising of the fused images. We present a novel ex ..."
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Abstract—Super-resolution (SR) is the process of combining multiple aliased low-quality images to produce a high-resolution high-quality image. Aside from registration and fusion of low-resolution images, a key process in SR is the restoration and denoising of the fused images. We present a novel extension of the combined Fourier-wavelet deconvolution and denoising algorithm ForWarD to the multiframe SR application. Our method first uses a fast Fourier-base multiframe image restoration to produce a sharp, yet noisy estimate of the high-resolution image. Our method then applies a space-variant nonlinear wavelet thresholding that addresses the nonstationarity inherent in resolution-enhanced fused images. We describe a computationally efficient method for implementing this space-variant processing that leverages the efficiency of the fast Fourier transform (FFT) to minimize complexity. Finally, we demonstrate the effectiveness of this algorithm for regular imagery as well as in digital mammography. 1 Index Terms—Digital X-ray imaging, multiframe deblurring, super-resolution (SR), wavelets, denoising.
REGULARIZED OPTIMIZATION FOR JOINT SUPER-RESOLUTION AND HIGH DYNAMIC RANGE IMAGE RECONSTRUCTION IN A PERCEPTUALLY UNIFORM DOMAIN
, 1520
"... This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s ..."
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This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s
Regularized super-resolution image reconstruction employing robust error norms
"... Abstract. A high-resolution image is reconstructed from a sequence of subpixel shifted, aliased low-resolution frames, by means of stochastic regularized super-resolution ͑SR͒ image reconstruction. The Tukey ͑T͒, Lorentzian ͑L͒, and Huber ͑H͒ cost functions are employed for the datafidelity term. T ..."
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Abstract. A high-resolution image is reconstructed from a sequence of subpixel shifted, aliased low-resolution frames, by means of stochastic regularized super-resolution ͑SR͒ image reconstruction. The Tukey ͑T͒, Lorentzian ͑L͒, and Huber ͑H͒ cost functions are employed for the datafidelity term. The performance of the particular error norms, in SR image reconstruction, is presented. Actually, their employment in SR reconstruction is preceded by dilating and scaling their influence functions to make them as similar as possible. Thus, the direct comparison of these norms in rejecting outliers takes place. The bilateral total variation ͑BTV͒ regularization is incorporated as a priori knowledge about the solution. The outliers effect is significantly reduced, and the high-frequency edge structures of the reconstructed image are preserved. The proposed TTV, LTV, and HTV methods are directly compared with a former SR method that employs the L 1 -norm in the data-fidelity term for synthesized and real sequences of frames. In the simulated experiments, noiseless frames as well as frames corrupted by salt-and-pepper noise are employed. Experimental results verify the robust statistics theory. Thus, the Tukey method performs best, while the L 1 -norm technique performs inferiorly to the proposed techniques.
Research Article Robust Color Image Superresolution: An Adaptive M-Estimation Framework
, 2008
"... This paper introduces a new color image superresolution algorithm in an adaptive, robust M-estimation framework. Using a robust error norm in the objective function, and adapting the estimation process to each of the low-resolution frames, the proposed method effectively suppresses the outliers due ..."
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This paper introduces a new color image superresolution algorithm in an adaptive, robust M-estimation framework. Using a robust error norm in the objective function, and adapting the estimation process to each of the low-resolution frames, the proposed method effectively suppresses the outliers due to violations of the assumed observation model, and results in color superresolution estimates with crisp details and no color artifacts, without the use of regularization. Experiments on both synthetic and real sequences demonstrate the superior performance over using the L2 and L1 error norms in the objective function. Copyright © 2008 N. A. El-Yamany and P. E. Papamichalis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
1 Super-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms
"... Abstract: Stochastic regularized methods are quite advantageous in Super-Resolution (SR) image reconstruction problems. In the particular techniques the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The present work ex-amines the effect of each o ..."
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Abstract: Stochastic regularized methods are quite advantageous in Super-Resolution (SR) image reconstruction problems. In the particular techniques the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The present work ex-amines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the Low-Resolution (LR) frames. Experimentation is car-ried out with the widely employed 2L, 1L, Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effec-tive SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.
