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

ABSTRACT The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution. Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters. The result of the approach is that an output sample close to signals with high certainty is interpolated using a small neighbourhood. An output sample close to signals with low certainty is spatially predicted from signals in a large neighbourhood.

  • [Show abstract] [Hide abstract]
    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
  • Source
    [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;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we describe a new strategy for using local structure adaptive filtering in normalized convolution. The shape of the filter, used as the applicability function in the context of normalized convolution, adapts to the local image structure and avoids filtering across borders. The size of the filter is also adaptable to the local sampling density to avoid unnecessary smoothing over high certainty regions. We compare our adaptive interpolation technique with the conventional normalized averaging methods. We found that our strat- egy yields a result that is much closer to the original signal both visually and in terms of MSE, meanwhile retaining sharpness and improving the SNR.
    Image Analysis, 13th Scandinavian Conference, SCIA 2003, Halmstad, Sweden, June 29 - July 2, 2003, Proceedings; 01/2003

Full-text (2 Sources)

Available from
May 16, 2014