Generative Plug and Play: Posterior Sampling for Inverse Problems

Over the past decade, Plug-and-Play (PnP) has become a popular method for reconstructing images using a modular framework consisting of a forward and prior model. The great strength of PnP is that an image denoiser can be used as a prior model while the forward model can be implemented using more traditional physics-based approaches. However, a limitation of PnP is that it reconstructs only a single deterministic image. In this paper, we introduce Generative Plug-and-Play (GPnP), a generalization of PnP to sample from the posterior distribution. As with PnP, GPnP has a modular framework using a physics-based forward model and an image denoising prior model. However, in GPnP these models are extended to become proximal generators, which sample from associated distributions. GPnP applies these proximal generators in alternation to produce samples from the posterior. We present experimental simulations using the well-known BM3D denoiser. Our results demonstrate that the GPnP method is robust, easy to implement, and produces intuitively reasonable samples from the posterior for sparse interpolation and tomographic reconstruction. Code to accompany this paper is available at https://github.com/gbuzzard/generative-pnp-allerton .Comment: 8 pages, submitted to 2023 IEEE Allerton Conferenc

Equations’ Derivations In The Three-Dimensional Super-Voxel Calculations

Model-Based Iterative Reconstruction (MBIR) is a widely-explored fully 3D Computed Tomography (CT) image reconstruction technique that has a large impact on the image re- construction community. The slow computation speed for MBIR, however, is a bottleneck for scientific advancements in fields that use imaging, such as materials. A recently proposed algorithm, Non-Uniform Parallel Super-Voxel (NU-PSV), utilizes the concept of Three-Dimensional Super-Voxel (3DSV) and Block-Transposed Buffer (BTB) [1]. Experiments in the past show that the NU- PSV algorithm significantly improves the computation speed for MBIR by regularizing data access pattern, reducing cache misses, enabling more parallelism and speeding up algorithmic convergence. This technical report serves as an auxiliary appendix to publication [1]. In this technical report, we demonstrate the theoretical calculations related to a BTB

Automated visual assembly inspection

Includes bibliographical references (pages 699-700).This chapter has discussed an intelligent assembly inspection system that uses a multiscale algorithm to detect errors in assemblies after the algorithm is trained on synthetic CAD images of correctly assembled products. It was shown how the CAD information of an assembly along with fast rendering techniques on specialized graphics machines can be used for the automation of the work-cell camera and light placement. The current emphasis in the manufacturing industry on concurrent engineering will only cause this integration between the CAD model of products and its manufacturing inspection to grow in value