A Real-time Endoscopic Image Denoising System
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Endoscopes featuring a miniaturized design have significantly enhanced
operational flexibility, portability, and diagnostic capability while
substantially reducing the invasiveness of medical procedures. Recently,
single-use endoscopes equipped with an ultra-compact analogue image sensor
measuring less than 1mm x 1mm bring revolutionary advancements to medical
diagnosis. They reduce the structural redundancy and large capital expenditures
associated with reusable devices, eliminate the risk of patient infections
caused by inadequate disinfection, and alleviate patient suffering. However,
the limited photosensitive area results in reduced photon capture per pixel,
requiring higher photon sensitivity settings to maintain adequate brightness.
In high-contrast medical imaging scenarios, the small-sized sensor exhibits a
constrained dynamic range, making it difficult to simultaneously capture
details in both highlights and shadows, and additional localized digital gain
is required to compensate. Moreover, the simplified circuit design and analog
signal transmission introduce additional noise sources. These factors
collectively contribute to significant noise issues in processed endoscopic
images. In this work, we developed a comprehensive noise model for analog image
sensors in medical endoscopes, addressing three primary noise types:
fixed-pattern noise, periodic banding noise, and mixed Poisson-Gaussian noise.
Building on this analysis, we propose a hybrid denoising system that
synergistically combines traditional image processing algorithms with advanced
learning-based techniques for captured raw frames from sensors. Experiments
demonstrate that our approach effectively reduces image noise without fine
detail loss or color distortion, while achieving real-time performance on FPGA
platforms and an average PSNR improvement from 21.16 to 33.05 on our test
dataset.
