April 5, 2024, 4:45 a.m. | Shangquan Sun, Wenqi Ren, Jingyang Peng, Fenglong Song, Xiaochun Cao

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.03327v1 Announce Type: new
Abstract: Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow. In this paper, we propose a new expression called Digital-Imaging Retinex theory (DI-Retinex) through theoretical and experimental analysis of Retinex theory in digital imaging. Our new expression includes an offset term in the enhancement model, which allows for pixel-wise brightness …

abstract arxiv cs.cv digital digital imaging dynamic eess.iv error image imaging light low noise overflow paper quantization theory type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Intelligence Manager

@ Sanofi | Budapest

Principal Engineer, Data (Hybrid)

@ Homebase | Toronto, Ontario, Canada