Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Proceedings of the conference on Visualization '98
Super-Resolution Reconstruction of Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Super-Resolution Imaging
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Robust color image superresolution: an adaptive M-estimation framework
Journal on Image and Video Processing - Color in Image and Video Processing
Region-Based Super Resolution for Video Sequences Considering Registration Error
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Region-based weighted-norm with adaptive regularization for resolution enhancement
Digital Signal Processing
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One critical aspect to achieve efficient implementations of image super-resolution is the need for accurate subpixel registration of the input images. The overall performance of super-resolution algorithms is particularly degraded in the presence of persistent outliers, for which registration has failed. To enhance the robustness of processing against this problem, we propose in this paper an integrated adaptive filtering method to reject the outlier image regions. In the process of combining the gradient images due to each low-resolution image, we use adaptive FIR filtering. The coefficients of the FIR filter are updated using the LMS algorithm, which automatically isolates the outlier image regions by decreasing the corresponding coefficients. The adaptation criterion of the LMS estimator is the error between the median of the samples from the LR images and the output of the FIR filter. Through simulated experiments on synthetic images and on real camera images, we show that the proposed technique performs well in the presence of motion outliers. This relatively simple and fast mechanism enables to add robustness in practical implementations of image super-resolution, while still being effective against Gaussian noise in the image formation model.