Many image denoising methods can be characterized as minimizing "loss + penalty," where the "loss" measures the fidelity of the denoised image to the data, and ...
This paper presents preliminary results on the fusion of denoised PET and SPECT data volumes from brushlet and wavelet thresholding methods. Texture-based brushlet denoising is we...
Andrew Laine, Elsa D. Angelini, Yinpeng Jin, Peter...
We address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse an...
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
The total variation model of Rudin, Osher, and Fatemi for image denoising is considered to be one of the best denoising models. In the past, its solutions were based on nonlinear ...