The following will be discussed in this chapter: Unconstrained Wiener filter structure, unconstrained Wiener filter solution, wiener deconvolution of a noisy blurred image. | Lecture Signals, systems & inference – Lecture 21: Wiener filtering illustrations Wiener filtering illustrations , Spring 2018 Lec 21 1 Unconstrained Wiener filter structure -mx my x[n] + h[·] + y[n] 2 Unconstrained Wiener filter solution -mx my Dyx(e jÆ) x[n] + H(e jÆ) = + y[n] Dxx(e jÆ) 3 .: Wiener “deconvolution” of a noisy blurred signal v[n] y[n] G[z] + H[z] y[n] r[n] x[n] Known, stable system Wiener flter 4 .: Wiener deconvolution of a noisy blurred image** Two-dimensional convolution + noise: P P x[k, l] = i j g[i, j]y[k - i, l - j] +v[k, l] **From 52007 Mathworks blog post by Prof. Stan Reeves, ECE Dept., Auburn University Wiener deconvolution of a noisy blurred image Mathworks blog posts by: Prof. Stan Reeves, ECE Dept., Auburn University Reeves, Stan. "Digital image processing using MATLAB: reading image files". MathWorks. Sept. 27, 2011. Reeves, Stan. "Image deblurring – Wiener filter." MathWorks. Nov. 2, 2007. 6 MIT OpenCourseWare Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: . 7
