/en/tags/frequency-domain/Recent content in Frequency Domain on Mi&Bee BlogHugo -- gohugo.ioen蓝宝石的傻话Fri, 12 Jun 2026 10:00:00 +0800/en/posts/physical-world/frequency-domain-restoration/Fri, 12 Jun 2026 10:00:00 +0800/en/posts/physical-world/frequency-domain-restoration/<h2 id="why-work-in-the-frequency-domain">Why Work in the Frequency Domain</h2> <p>In the previous post we built the image degradation model: the observed image $g(x,y)$ is the original image $f(x,y)$ convolved with a degradation function $h(x,y)$ and corrupted by additive noise $n(x,y)$:</p> $$g(x,y) = h(x,y) * f(x,y) + n(x,y)$$<p>Restoration means: given $g$ and $h$, recover $f$ as closely as possible.</p> <p>In the spatial domain, this requires solving a deconvolution problem. Deconvolution is a massive linear system. For a $512 \times 512$ image, you face 260,000 unknowns. Direct solving is practically impossible.</p>