A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Wavelet-based diffusion approaches for signal denoising
Signal Processing
Image histogram thresholding based on multiobjective optimization
Signal Processing
An anisotropic diffusion-based defect detection for low-contrast glass substrates
Image and Vision Computing
An improved anisotropic diffusion model for detail- and edge-preserving smoothing
Pattern Recognition Letters
Brightness preserving histogram equalization with maximum entropy: a variational perspective
IEEE Transactions on Consumer Electronics
A Dynamic Histogram Equalization for Image Contrast Enhancement
IEEE Transactions on Consumer Electronics
Image enhancement and denoising by complex diffusion processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive thresholding by variational method
IEEE Transactions on Image Processing
Forward-and-backward diffusion processes for adaptive image enhancement and denoising
IEEE Transactions on Image Processing
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It is very difficult in low contrast images to distinguish between the noisy background and the regions of low gray level inter-region edges. The classical Perona-Malik anisotropic diffusion is able to smooth the defective background but cannot enhance faultless low gray level inter-region edges in such low contrast images. The proposed method provides an unsupervised machine learning process to modify the anisotropic diffusion by generating an adaptive threshold in diffusion coefficient function using statistical measures. In the proposed method, image histogram is employed to calculate the global gray level variance over the entire image and local gray level variance over the defined neighborhood of each pixel of given image. The adaptive threshold in diffusion coefficient function varies in accordance with the difference between the two variances which gives a measure of intensity contrast in that neighborhood. The experimental results from various low contrast images have shown that the proposed unsupervised machine learning approach for adaptive threshold selection in anisotropic diffusion can effectively smooth noisy background with preservation of low gradient edges.