Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging
This work addresses convergence guarantees for PnP methods in imaging, which is crucial for researchers and practitioners in computational imaging and data fusion, though it is incremental as it builds on existing PnP frameworks.
The paper tackles the convergence issue in plug-and-play (PnP) methods for imaging inverse problems by proposing a scene-adapted prior integrated into ADMM, proving algorithm convergence, and applies it to hyperspectral sharpening/fusion and image deblurring, showing improved performance in these applications.
The recently proposed plug-and-play (PnP) framework allows leveraging recent developments in image denoising to tackle other, more involved, imaging inverse problems. In a PnP method, a black-box denoiser is plugged into an iterative algorithm, taking the place of a formal denoising step that corresponds to the proximity operator of some convex regularizer. While this approach offers flexibility and excellent performance, convergence of the resulting algorithm may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. In this paper, we propose a PnP approach where a scene-adapted prior (i.e., where the denoiser is targeted to the specific scene being imaged) is plugged into ADMM (alternating direction method of multipliers), and prove convergence of the resulting algorithm. Finally, we apply the proposed framework in two different imaging inverse problems: hyperspectral sharpening/fusion and image deblurring from blurred/noisy image pairs.