Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN
Riki Numata, Hyunwoo Kim, Seiya Ono, Myungjin Cho, Min‐Chul Lee · 2023
Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed.