基于像素暗噪声估计的EBAPS图像自适应小波阈值降噪  

Adaptive Wavelet Threshold Denoising Based on Pixel Dark Noise of EBAPS

作　　者：刘璇[1] 李炳臻 李力[1] 金伟其[1] 程宏昌 Liu Xuan;Li Bingzhen;Li Li;Jin Weiqi;Cheng Hongchang(Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,Beijing Institute of Technology,Beijing 100081,China;Science and Technology on Low-Light-Level Night Vision Laboratory,Xi’an 710065,Shaanxi,China)

机构地区：[1]北京理工大学光电成像技术与系统教育部重点实验室,北京100081 [2]微光夜视技术重点实验室,陕西西安710065

出　　处：《光学学报》2024年第16期76-87,共12页Acta Optica Sinica

基　　金：“十四五”装备预研基金(50914020206)。

摘　　要：电子轰击有源像素传感器(EBAPS)是一种真空-固体混合的高性能微光视频成像器件,国产EBAPS尚处于研究初期,其成像环节会引入各种噪声,而经典常见的降噪算法大多针对高斯噪声,且需要已知噪声方差水平,因此需要研究特定的图像降噪算法。首先,分析EBAPS的噪声特性,包含与信号强度无关的高斯噪声、随信号强度变化的泊松噪声,以及固定噪声。然后,利用EBAPS的暗像素结构特性,提出一种单帧图像的噪声强度估计方法。最后,根据估计出的噪声强度对传统的小波阈值降噪进行改进,根据单帧图像的噪声强度分布提出一种自适应可变阈值。实验结果表明,所提出的方法有效降低了EBAPS图像的随机噪声。Objective Electron bombarded active pixel sensor(EBAPS)is a kind of high-performance low-light video imaging device with vacuum-solid mixture.Since the domestic EBAPS is still in the early stage of research,various noises are unavoidable during the imaging process.However,the classical denoising algorithms,such as total variation,wavelet,and various edge-preserving filters,are aimed at additive white Gaussian noise(AWGN)with constant standard deviation,and the noise variance level should be known.Since the noise of EBAPS is a mixture of dark noise,shot noise,and fixed pattern noise,with unknown noise level,the denoising algorithms designed for AWGN are not effective for EBAPS images.Therefore,we first analyze the noise characteristics of EBAPS,including AWGN independent of signal strength,Poisson noise varying with signal strength,and fixed noise.Then,we propose a noise estimation method for a single frame image by employing dark pixel structure characteristics of EBAPS.Finally,the traditional wavelet threshold denoising is improved according to the estimated noise intensity,and an adaptive variable threshold is put forward according to the noise intensity of the single frame image.We hope that our denoising method can improve the low-light image quality of EBAPS with lower computation and less frames.Methods The proposed algorithm includes noise estimation and wavelet denoising.The noise estimation includes the following steps.First,the properties of the solid-state imaging device and vacuum imaging device are combined to infer the noise source of EBAPS,and the relationship between EBAPS noise and signal intensity is obtained by experiments using the photo transfer curve(PTC)method.Then,based on the dark pixel structure of EBAPS,we infer the unified noise intensity model using a single frame image.The wavelet denoising decomposes the image into multiple sub-bands at different resolutions and scales,the image subject information exits in the low-frequency sub-band,and the noise and detail information exits in the

关 键 词：图像处理 EBAPS 像素暗噪声 自适应小波阈值 噪声强度估计 图像降噪 

分 类 号：O439[机械工程—光学工程]