Added a new test script for deconvolution

examples/test_deconv_sv_psf.py

@@ -0,0 +1,39 @@
+r"""
+SV_DECONV
+=====
+This example implements the saptially varying PSF deconvolution algorithm
+based on: Flicker & Rigaut, 2005 https://doi.org/10.1364/JOSAA.22.000504
+"""
+
+import numpy as np
+import pyunlocbox as plx
+from scipy.signal import fftconvolve
+
+###############################################################################
+# Sample data
+im = np.random.randn(2748, 3840)
+W = np.random.randn(20, np.prod(im.shape))
+U = np.random.randn(np.prod(im.shape), 20)
+
+# Setup Forward and Adjoint Models and Create Solver
+def forward(im):
+    im_sim = np.zeros_like(im)
+    for i in range(15):
+        weight = W[i,:].reshape(*im.shape)
+        psf_mode = U[:,i].reshape(*im.shape)
+        im_sim += fftconvolve(im* weight, psf_mode, mode='same')
+    return im_sim
+
+def forward_adj(im):
+    im_sim = np.zeros_like(im)
+    for i in range(15):
+        weight = W[i,:].reshape(*im.shape)
+        psf_mode = U[:,i].reshape(*im.shape)
+        im_sim += fftconvolve(im, np.flipud(np.fliplr(psf_mode)), mode='same')* weight
+    return im_sim
+
+tau=10
+f1 = plx.functions.norm_l2(y=im, A=forward, At=forward_adj, lambda_=tau)
+f2 = plx.functions.norm_tv(maxit=50, dim=2)
+solver = plx.solvers.forward_backward(step=0.5/tau, accel=plx.acceleration.fista())
+ret = plx.solvers.solve([f1, f2], x0=im.copy(), solver=solver, maxit=10, verbosity='ALL')