Computer Vision, Graphics, and Image Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
DCC '97 Proceedings of the Conference on Data Compression
Space-frequency quantization for wavelet image coding
IEEE Transactions on Image Processing
Image segmentation and edge enhancement with stabilized inverse diffusion equations
IEEE Transactions on Image Processing
High performance scalable image compression with EBCOT
IEEE Transactions on Image Processing
Edge-directed prediction for lossless compression of natural images
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Sweeping fingerprint verification system based on template matching
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
A sweeping fingerprint verification system using the template matching method
WSEAS Transactions on Computers
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Scale-space representation has been extensively studied in the computer vision community for analyzing image structures at different scales. This paper borrows and develops useful mathematical tools from scale-space theory to facilitate the task of image compression. Instead of compressing the original image directly, we propose to compress its scale-space representation obtained by the forward diffusion with a Gaussian kernel at the chosen scale. The major contribution of this work is a novel solution to the ill-posed inverse diffusion problem. We analytically derive a nonlinear filter to deblur Gaussian blurring for 1D ideal step edges. The generalized 2D edge enhancing filter only requires the knowledge of local minimum/maximum and preserves the geometric constraint of edges. When combined with a standard wavelet-based image coder, the forward and inverse diffusion can be viewed as a pair of pre-processing and post-processing stages used to select and preserve important image features at the given bit rate. Experiment results have shown that the proposed diffusion-based techniques can dramatically improve the visual quality of reconstructed images at low bit rate (below 0.25bpp).