Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
A MAP Algorithm to Super-Resolution Image Reconstruction
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Video Super-Resolution Using Controlled Subpixel Detector Shifts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast sub-pixel motion estimation techniques having lower computational complexity
IEEE Transactions on Consumer Electronics
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
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
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Traditionally, image enlargement is magnified from a single image. Due to the one and only one image, the quality of the reconstructed image is thus constrained. Super-resolution is proposed to use multiple frames as additional information to estimate the high-resolution image. If we have enough low-resolution images with observed subpixels, the high-resolution image can be reconstructed. In this paper, a complete super-resolution model based on k-means motion clustering is proposed for image enlargement with multiple moving objects. Motion masks are first produced for useful image selection and then blocks matching are used to do motion estimation. Two complementary features, sum of absolute difference (SAD) and normalized cross-correlation (NCC), are adopted as the matching criterion. Objects are assumed to move slightly between two consecutive images. Thus, erroneous motion vectors could be corrected by the center of motion clusters. The proposed method achieves better magnification quality than the traditional ones and some previous super resolution works. From the experimental results, both the visual and quantitative improvements, especially in the high frequency, are significant.