Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
From two-dimensional nonlinear diffusion to coupled Haar wavelet shrinkage
Journal of Visual Communication and Image Representation
Correspondences between wavelet shrinkage and nonlinear diffusion
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Denoising by singularity detection
IEEE Transactions on Signal Processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Forward-and-backward diffusion processes for adaptive image enhancement and denoising
IEEE Transactions on Image Processing
A new iterated two-band diffusion equation: theory and its application
IEEE Transactions on Image Processing
Image denoising based on wavelets and multifractals for singularity detection
IEEE Transactions on Image Processing
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Infrared images are characterized by small signal-to-noise ratio and low contrast thus making it much difficult to achieve infrared image denoising and edge enhancement. The paper proposes a novel method based on wavelet diffusion, which reduces noise in infrared images while enhancing and preserving edges. First, wavelet modulus and local singularity are combined into a joint conditional model to build an elementary edge map, then the edge map are updated with geometric consistency for better discrimination of edges. To enhancing edges simultaneously, forward and backward diffusion technique is introduced into wavelet diffusion. Finally, the equation of forward and backward diffusion is redesigned according to the new edge map. Experimental results demonstrate that the new proposed method can effectively realize infrared image denoising and edge enhancement.