A Gradient-Based Adaptive Interpolation Filter for Multiple View Synthesis
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Super-resolution in medical imaging: an illustrative approach through ultrasound
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Invariant image reconstruction from irregular samples and hexagonal grid splines
Image and Vision Computing
Realizing super-resolution with a directional anisotropic correlation model
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Local object-based super-resolution mosaicing from low-resolution video
Signal Processing
Resolution enhancement by vibrating displays
ACM Transactions on Graphics (TOG)
Journal of Mathematical Imaging and Vision
A unified framework for multi-sensor HDR video reconstruction
Image Communication
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A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.