wiener2 matlab function  (MathWorks Inc)

Journal: Proceedings of the National Academy of Sciences of the United States of America

Article Title: Coarse to fine dynamics of monocular and binocular processing in human pattern vision

doi: 10.1073/pnas.1101246108

Figure Lengend Snippet: Monocular filters and binocular interaction matrix (aggregate observer; >60,000 trials). A and B show monocular filters obtained by applying psychophysical reverse correlation to the noise probe pictured in Fig. 1 J and K. We computed a binocular interaction matrix for each stereo frame of the stimulus (Materials and Methods); for example, the binocular interaction matrix in C corresponds to the monocular spatial profiles indicated by cyan and magenta dashed outlines in A and B. J shows the (time) average across C–I. The yellow line shows the marginal average across the diagonal axis orthogonal to it; similarly, the white line refers to the other diagonal dimension [which defines isodisparity points (8)]. All surfaces plot Z scores (colored for |Z| > 2). Contour levels (red for positive and blue for negative) correspond to interpolated and denoised (using Matlab wiener2 function) images of the filter (introduced to aid visualization of surface structure).

Article Snippet: Contour levels (red for positive and blue for negative) correspond to interpolated and denoised (using Matlab wiener2 function) images of the filter (introduced to aid visualization of surface structure).

Techniques: