Operator Sketching for Deep Unrolling Networks
This addresses computational bottlenecks for researchers and practitioners in medical imaging, offering an incremental improvement over stochastic unrolling methods.
The paper tackles the inefficiency of deep unrolling networks in high-dimensional imaging tasks like 3D CT and 4D MRI by proposing operator sketching to approximate products in image space, achieving significant acceleration and compression as demonstrated in X-ray CT reconstruction experiments.
In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching. The deep unrolling networks are currently the state-of-the-art solutions for imaging inverse problems. However, for high-dimensional imaging tasks, especially the 3D cone-beam X-ray CT and 4D MRI imaging, the deep unrolling schemes typically become inefficient both in terms of memory and computation, due to the need of computing multiple times the high-dimensional forward and adjoint operators. Recently researchers have found that such limitations can be partially addressed by stochastic unrolling with subsets of operators, inspired by the success of stochastic first-order optimization. In this work, we propose a further acceleration upon stochastic unrolling, using sketching techniques to approximate products in the high-dimensional image space. The operator sketching can be jointly applied with stochastic unrolling for the best acceleration and compression performance. Our numerical experiments on X-ray CT image reconstruction demonstrate the remarkable effectiveness of our sketched unrolling schemes.