The paper "Deep Image Prior" by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky explores the idea that the structure of a generator network can capture significant low-level image statistics prior to any learning. The authors demonstrate that a randomly-initialized neural network can serve as a handcrafted prior, achieving excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. The same prior can also be used to invert deep neural representations and restore images based on flash-no flash input pairs. The approach highlights the inductive bias captured by standard generator network architectures and bridges the gap between learning-based methods and learning-free methods based on handcrafted image priors. The paper includes detailed experiments and comparisons with state-of-the-art methods, showing that the deep image prior can produce results comparable to or better than those of learned priors in various image restoration tasks.The paper "Deep Image Prior" by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky explores the idea that the structure of a generator network can capture significant low-level image statistics prior to any learning. The authors demonstrate that a randomly-initialized neural network can serve as a handcrafted prior, achieving excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. The same prior can also be used to invert deep neural representations and restore images based on flash-no flash input pairs. The approach highlights the inductive bias captured by standard generator network architectures and bridges the gap between learning-based methods and learning-free methods based on handcrafted image priors. The paper includes detailed experiments and comparisons with state-of-the-art methods, showing that the deep image prior can produce results comparable to or better than those of learned priors in various image restoration tasks.