K-SVD denoising is a well-known algorithm, based on local sparsity modeling of image patches. Concieved in 2006, this algorithm was based on dictionary learning, achieveing (at that time) state-of-the-art performance. Over the years, better methods appeared, slowly and gradually shadowing this algorithm and pushing it to the back seats of image processing. With the entrance of supervised deep-learning denoising methods, this trend further strengthened. In our recent paper (co-authored by Meyer Scetbon, Peyman Milanfar and myself), we bring new life to the K-SVD denoising algorithm, by unfolding it to a network and training it end-to-end. Beyond the substantial improvement in performance, this result poses intruiging thoughts about how deep network architectures should be created, how classical image processinng algorithms should influence this, and more.