Blind Deconvolution Image Restoration Methods
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In this article, we will demonstrate how to perform image restoration on blurred images using blind deconvolution methods. This approach can significantly enhance image quality and yield remarkably improved results. The restoration process makes images clearer and enhances detail visibility, which is crucial for various application domains such as medical imaging, photography, and image recognition systems. The implementation typically involves two key phases: estimating the unknown blur kernel (point spread function) through algorithms like maximum a posteriori estimation or blind deconvolution networks, followed by non-blind deconvolution using methods such as Richardson-Lucy deconvolution or Wiener filtering. Mastering these image restoration techniques is therefore essential for improving both image quality and practical application effectiveness across multiple fields.
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