DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization

TL;DR

This paper introduces DDGM, a simple diffusion-inspired method combining gradient-based minimization and denoising for inverse problems, demonstrating high accuracy in tomographic reconstruction with fewer steps than complex diffusion models.

Contribution

The paper proposes a novel, simplified approach that integrates traditional gradient minimization with denoising, avoiding SVD and backpropagation through denoisers, and extends to large images.

Findings

Achieves high accuracy with as few as 50 denoising steps.

Outperforms complex diffusion methods like DDRM and DPS in tomographic reconstruction.

Effective on large, arbitrary-sized images.

Abstract

Inverse problems generally require a regularizer or prior for a good solution. A recent trend is to train a convolutional net to denoise images, and use this net as a prior when solving the inverse problem. Several proposals depend on a singular value decomposition of the forward operator, and several others backpropagate through the denoising net at runtime. Here we propose a simpler approach that combines the traditional gradient-based minimization of reconstruction error with denoising. Noise is also added at each step, so the iterative dynamics resembles a Langevin or diffusion process. Both the level of added noise and the size of the denoising step decay exponentially with time. We apply our method to the problem of tomographic reconstruction from electron micrographs acquired at multiple tilt angles. With empirical studies using simulated tilt views, we find parameter settings for our method that produce good results. We show that high accuracy can be achieved with as few as 50 denoising steps. We also compare with DDRM and DPS, more complex diffusion methods of the kinds mentioned above. These methods are less accurate (as measured by MSE and SSIM) for our tomography problem, even after the generation hyperparameters are optimized. Finally we extend our method to reconstruction of arbitrary-sized images and show results on 128 $\times$ 1568 pixel images