# Forskjeller

Her vises forskjeller mellom den valgte versjonen og den nåværende versjonen av dokumentet.

 tma4205:2017h:downloads [2017-10-12]markug tma4205:2017h:downloads [2017-10-12] (nåværende versjon)markug Begge sider forrige revisjon Forrige revisjon 2017-10-12 markug 2017-10-12 markug 2017-10-12 markug opprettet 2017-10-12 markug 2017-10-12 markug 2017-10-12 markug opprettet Linje 9: Linje 9: * Implementation of the numerical gradient: {{ :​tma4205:​2017h:​num_grad.m |}} * Implementation of the numerical gradient: {{ :​tma4205:​2017h:​num_grad.m |}} * Implementation of the numerical divergence: {{ :​tma4205:​2017h:​num_divergence.m |}} * Implementation of the numerical divergence: {{ :​tma4205:​2017h:​num_divergence.m |}} + + The code implements the numerical solution of the PDE $u-\lambda\textrm{div}(a \nabla u) = f$, where $a$ is some edge indicator function, which is small near edges of the image $f$, and large in homogeneous regions of $f$. The input for both the CG and PCG implementation should be a colour image, that is, a 3d array of size $m \times n \times 3$ (the noisy image). The first output is again a 3d array of the same size (the denoised imaged), and the second output is a vector containing the sizes of the residuals in each step (if one is interested in checking how the methods behave). Note that the code won't work on gray scale images! The input for both the CG and PCG implementation should be a colour image, that is, a 3d array of size $m \times n \times 3$ (the noisy image). The first output is again a 3d array of the same size (the denoised imaged), and the second output is a vector containing the sizes of the residuals in each step (if one is interested in checking how the methods behave). Note that the code won't work on gray scale images!