Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Nonlinear Image Filtering with Edge and Corner Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive implementation of the Gaussian filter
Signal Processing
Filling-in by joint interpolation of vector fields and gray levels
IEEE Transactions on Image Processing
A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Low complexity dense motion estimation using phase correlation
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images
Journal of Mathematical Imaging and Vision
Genetic normalized convolution
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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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 sample density to avoid unnecessary smoothing over high certainty regions. We compared our adaptive interpolation technique with conventional normalized averaging methods. We found that our strategy 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.