A CT Image Denoising Method Based on Projection Domain Feature

TL;DR

This paper introduces a projection domain denoising algorithm for industrial CT images that leverages neighboring view similarities to reduce noise, improving image quality without increasing exposure or sampling.

Contribution

It presents a novel denoising method based on projection domain features that effectively reduces noise using neighboring view similarities, validated through simulations and real data.

Findings

Effective noise reduction in CT projections

Improved image quality without increased exposure

Validated with numerical and practical experiments

Abstract

In order to improve image quality of projection in industrial applications, generally, a standard method is to increase the current or exposure time, which might cause overexposure of detector units in areas of thin objects or backgrounds. Increasing the projection sampling is a better method to address the issue, but it also leads to significant noise in the reconstructed image. This paper proposed a projection domain denoising algorithm based on the features of the projection domain for this case. This algorithm utilized the similarity of projections of neighboring veiws to reduce image noise quickly and effectively. The availability of the algorithm proposed in this work has been conducted by numerical simulation and practical data experiments.