Joint qualitative and quantitative evaluation of fast image dehazing based on dark channel prior

Abstract

A polarization filter array (PFA) camera is an imaging device capable of analyzing the polarization state of light in a snapshot manner. These cameras exhibit spatial variations, i.e., nonuniformity, in their response due to optical imperfections introduced during the nanofabrication process. Calibration is done by computational imaging algorithms to correct the data for radiometric and polarimetric errors. We reviewed existing calibration methods and applied them using a practical optical acquisition setup and a commercially available PFA camera. The goal of the evaluation is first to compare which algorithm performs better with regard to polarization error and then to investigate both the influence of the dynamic range and number of polarization angle stimuli of the training data. To our knowledge, this has not been done in previous work.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

“…It is imperative to acknowledge that the diminished intensity observed in the dark channel primarily stems from shadows cast by the scene, the presence of dark objects, and vividly colored surfaces or objects. The dark channel a priori defogging algorithm, grounded in statistical principles, has demonstrated commendable outcomes within the realm of image defogging, exhibiting greater stability in comparison to the aforementioned non-physical model algorithms [34][35][36][37][38]. This algorithm can more accurately estimate the thickness of the fog, resulting in a more natural and clearer defogging effect [39][40][41].…”