Conference Paper

# Continuous normalized convolution

Dept. of Biomed. Eng., Linkoping Univ., Sweden

DOI: 10.1109/ICME.2002.1035884 Conference: Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on, Volume: 1 Source: IEEE Xplore

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**ABSTRACT:**This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion is estimated using previously decoded field-images. An initial motion compen-sated prediction is produced using the assumption that the motion is predictable in time. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the spe-cific characteristics of the field-image to be coded, the initially pre-dicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.10/2002; -
##### Conference Paper: Low complexity dense motion estimation using phase correlation

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**ABSTRACT:**We propose a low-complexity dense motion estimation scheme particularly attractive for real-time video applications. Our scheme is based on overlapped block-based motion estimation using phase correlation at critical pixel locations. These form an irregularly sampled grid capturing salient motion features of a scene. The dense vector field is obtained by applying normalized convolution on the irregular grid. Our experiments show that our scheme provides reliable sub-pixel accuracy motion vectors corresponding to actual scene motion, outperforms differential and phase-based methods and yields comparable performance to more complex and time consuming robust motion estimation techniques.Digital Signal Processing, 2009 16th International Conference on; 08/2009 - [Show abstract] [Hide abstract]

**ABSTRACT:**A fast method for super-resolution (SR) recon- struction from low resolution (LR) frames with known registration is proposed. The irregular LR samples are incorporated into the SR grid by stamp- ing into 4-nearest neighbors with position certainties. The signal certainty reects the errors in the LR pix- els' positions (computed by cross-correlation or optic o w) and their intensities. Adaptive normalized aver- aging is used in the fusion stage to enhance local lin- ear structure and minimize further blurring. The local structure descriptors including orientation, anisotropy and curvature are computed directly on the SR grid and used as steering parameters for the fusion. The optimum scale for local fusion is achieved by a sam- ple density transform, which is also presented for the rst time in this paper.ASCI. 01/2004;

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