Show HN: ReflexConv2d – 57% less blur in image reconstruction

- Image reconstruction is the process of restoring a high-quality image from degraded or low-resolution data — essential in medical imaging, photography, and computer vision. - ReflexConv2D is a new convolutional layer (the core building block of neural networks used for image processing) that claims to reduce "blurring" artifacts by 57% compared to standard approaches. - The "Reflex" in the name likely refers to a mechanism that feeds information back through the network, similar to how biological vision systems use feedback loops to sharpen perception — an alternative to the purely feedforward design of most convolutional layers. - This is a Show HN, meaning the author is presenting their own open-source project to the Hacker News community — the project is hosted on GitHub, so it's freely available code, not a commercial product. - The benchmark of "57% less blur" is likely measured against standard convolutional layers like those in PyTorch or TensorFlow, possibly using metrics such as PSNR (peak signal-to-noise ratio) or SSIM (structural similarity).