Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Wavelet Algorithms for High-Resolution Image Reconstruction
SIAM Journal on Scientific Computing
A frequency domain approach to registration of aliased images with application to super-resolution
EURASIP Journal on Applied Signal Processing
PDE-based deconvolution with forward-backward diffusivities and diffusion tensors
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
Improving image resolution by adaptive back-projection correction techniques
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
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In several application areas such as art, medicine, broadcasting and e-commerce, high-resolution images are needed. Super-resolution is the algorithmic process of increasing the resolution of an image given a set of displaced low-resolution, noisy and degraded images. In this paper, we present a new super-resolution algorithm based on the generalized sampling theorem of Papoulis and wavelet decomposition. Our algorithm uses an area-pixel model rather than a point-pixel model. The sampling theorem is used for merging a set of low-resolution images into a high-resolution image, and the wavelet decomposition is used for enhancing the image through efficient noise removing and high-frequency enhancement. The proposed algorithm is non-iterative and not time-consuming. We have tested our algorithm on multiple images and used the peak-to-noise ratio, the structural similarity index and the relative error as quality measures. The results show that our algorithm gives images of good quality.