LFAD: Locally- and Feature-Adaptive Diffusion based Image Denoising
Ajay Kumar Mandava, Emma E. Regentova, George N. Bebis · Applied Mathematics & Information Sciences · 2013
LFAD is a novel locally and feature adaptive diffusion method for denoising an additive Gaussian noise in images.The method approaches each image region individually and uses different number of diffusion iterations per region for attaining best objective quality according to PSNR.Unlike block-transform based methods which perform with a predetermined optimum block size and clustering-based denoising methods which use a fixed optimum number of classes, our method searches for an optimum patch size through iterative diffusion starting with a small patch size and proceeds with aggregating patches until a best PSNR is attained.The diffusion model has a substitution of the gradient value with the inverse difference moment (IDM) which is a robust feature in determining the amount of local intensity variation in the presence of noise.Experiments with benchmark images and various noise levels show that the designed LFAD outperforms advanced diffusion based denoising methods, and it is competitive with the state-of-the-art block-transformed techniques by yielded PSNR levels, producing however lesser visible blocking or ringing artifacts.