Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Communications of the ACM
Curvature Estimation in Oriented Patterns Using Curvilinear Models Applied to Gradient Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
Super resolutionwith probabilistic motion estimation
IEEE Transactions on Image Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
A spatially adaptive nonparametric regression image deblurring
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
Deblurring Using Regularized Locally Adaptive Kernel Regression
IEEE Transactions on Image Processing
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The classic multi-image-based super-resolution (SR) methods typically take global motion pattern to produce one or multiple high-resolution (HR) versions from a set of low-resolution (LR) images. However, due to the influence of aliasing and noise, it is difficult to obtain highly accurate registration with sub-pixel accuracy. Moreover, in practical applications, the global motion pattern is rarely found in the real LR inputs. In this paper, to surmount or at least reduce the aforementioned problems, we develop a novel SR framework for video sequence by extending the traditional 2-dimentional (2D) normalized convolution (NC) to 3-dimentional (3D) case. In the proposed framework, to bypass explicit motion estimation, we estimate a target pixel by taking a weighted average of pixels from its neighborhood. We further up-scale the input video sequence in temporal dimension based on the extended 3D NC and hence more video frames can be generated. Fundamental experiments demonstrate the effectiveness of the proposed SR framework both quantitatively and perceptually.