-julia> res, o = deconvolution(img_n, psf);</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/roflmaostc/DeconvOptim.jl/blob/af341a3a4dc375b5b0e0f32eeede0f77dd9bbfe9/src/deconvolution.jl#L3-L59">source</a></section></details></article><article><details class="docstring" open="true"><summary id="DeconvOptim.richardson_lucy_iterative"><a class="docstring-binding" href="#DeconvOptim.richardson_lucy_iterative"><code>DeconvOptim.richardson_lucy_iterative</code></a> — <span class="docstring-category">Function</span></summary><section><div><pre><code class="language-julia hljs">richardson_lucy_iterative(measured, psf; <keyword arguments>)</code></pre><p>Classical iterative Richardson-Lucy iteration scheme for deconvolution. <code>measured</code> is the measured array and <code>psf</code> the point spread function. Converges slower than the optimization approach of <code>deconvolution</code></p><p><strong>Keyword Arguments</strong></p><ul><li><code>regularizer=GR()</code>: A regularizer function. Can be exchanged</li><li><code>λ=0.05</code>: A float indicating the total weighting of the regularizer with respect to the global loss function</li><li><code>iterations=100</code>: Specifies number of iterations.</li><li><code>progress</code>: if not <code>nothing</code>, the progress will be monitored in a summary dictionary as obtained by DeconvOptim.options<em>trace</em>deconv()</li></ul><p><strong>Example</strong></p><pre><code class="language-julia-repl hljs">julia> using DeconvOptim, TestImages, Colors, Noise;
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