Journal of Computational Physics
International Journal of Remote Sensing
A New Information Measure for Natural Images
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Infinite Photography: New Mathematical Model for High-Resolution Images
Journal of Mathematical Imaging and Vision
An optimized algorithm for the evaluation of local singularity exponents in digital signals
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Reconstruction of speech signals from their unpredictable points manifold
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Reconstructing an image from its edge representation
Digital Signal Processing
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Real-world images are complex objects, difficult to describe but at the same time possessing a high degree of redundancy. A previous on the statistical properties of natural images reveals that natural images can be viewed through different partitions which are essentially fractal in nature. One particular fractal component, related to the most singular (sharpest) transitions in the image, seems to be highly informative about the whole scene. We show how to decompose the image into their fractal components. We see that the most singular component is related to (but not coincident with) the edges of the objects present in the scenes. We propose a new, simple method to reconstruct the image with information contained in that most informative component. We see that the quality of the reconstruction is strongly dependent on the capability to extract the relevant edges in the determination of the most singular set. We discuss the results from the perspective of coding, proposing this method as a starting point for future developments