Inverse problems are situations where hidden information is computed from external observations. For instance, in image deblurring, we aim to recover a clear image from one that is blurred and noisy. This first tutorial will explore how matrices and vectors naturally model discrete, ill-posed inverse problems, using linear algebra techniques to understand the inherent challenges. We will then introduce filtering and variational approaches to regularize these problems, effectively allowing us to recover useful information from seemingly distorted data
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