Diffusion model for gradient preconditioning in hyperspectral imaging inverse problems

TL;DR

This paper introduces a novel diffusion-based framework for gradient preconditioning in hyperspectral imaging inverse problems, enhancing convergence and reconstruction quality from limited measurements.

Contribution

It proposes using denoising diffusion models to learn a reverse process in gradient space, effectively preconditioning optimization in high-dimensional inverse problems.

Findings

Significant improvements in reconstruction accuracy.

Enhanced stability and convergence in limited data regimes.

Effective integration of generative models with inverse problem solving.

Abstract

Recovering high-dimensional statistical structure from limited measurements is a fundamental challenge in hyperspectral imaging, where capturing full-resolution data is often infeasible due to sensor, bandwidth, or acquisition constraints. A common workaround is to partition measurements and estimate local statistics-such as the covariance matrix-using only partial observations. However, this strategy introduces noise in the optimization gradients, especially when each partition contains few samples. In this work, we reinterpret this accumulation of gradient noise as a diffusion process, where successive partitions inject increasing uncertainty into the learning signal. Building on this insight, we propose a novel framework that leverages denoising diffusion models to learn a reverse process in gradient space. The model is trained to map noisy gradient estimates toward clean, well-conditioned updates, effectively preconditioning the optimization. Our approach bridges generative modeling and inverse problem solving, improving convergence and reconstruction quality under aggressive sampling regimes. We validate our method on hyperspectral recovery tasks, demonstrating significant gains in accuracy and stability over traditional optimization pipelines.