基于视频时序相关特性的γ射线噪声去除方法研究

Gamma-Ray Noise Removal Based on Video Time Series Correlation

针对γ辐射环境下COMS图像传感器所获场景图像中存在斑块噪声的问题,提出了一种基于视频时序相关特性的γ辐射场景图像噪声去除方法。首先根据γ辐射场景视频时序相关特性中的背景相关特性与前景噪斑的瞬态特性,使用帧差法与统计分析法从视频序列图像的残差中得到γ辐射场景图像中亮、暗噪斑的位置分布。然后通过依据CMOS图像传感器承受的累计辐照剂量所设计的帧数判断模型,得到对当前帧图像实现有效修复所需的临近帧图像,再结合亮、暗噪斑的位置分布及噪斑的瞬态特性,根据设计的自适应阈值机制,获得临近帧图像与当前帧图像噪斑位置相同且未受辐射干扰的有效像素值集合,并使用该集合像素值的均值对噪声像素进行恢复。最后采用拉普拉斯锐化滤波器对图像进行后处理以进一步提高图像质量。实验结果表明,所提方法与多种去噪方法相比具有更高的峰值信噪比、结构相似性与主观感知满意度,表明其去噪效率高、细节保留丰富。

We proposed an approach to remove the noise in theγradiation scene image based on the video timeseries correlation considering the challenges of patch noise in the scene images generated using the complementary metaloxidesemiconductor(CMOS)image sensor in aγradiation environment.First,according to the foreground patch noise’s backgroundrelated and transient characteristics in theγradiation scene video,which are both included in the time series correlation characteristics,the frame difference and statistical analysis approaches are employed to generate the bright and dark patch noise’s location distribution in theγradiation scene image from the video sequence image’s residual.Then,through the frame number judgment model designed by the cumulative radiation dose borne using the CMOS image sensor,the adjacent frame images required to effectively repair the current frame image are generated.The effective pixel value is set in the adjacent frame with the same position as the current frame image patch noise and is not affected by radiation interference using the adaptive threshold mechanism and location distribution of bright and dark patch noise and transient characteristics of the patch noise,and the effective pixel value’s mean value is employed to recover the noise pixels.Finally,the Laplacian sharpening filter is used for image postprocessing to enhance the image quality.Experimental results demonstrate that the proposed approach has a higher peak signaltonoise ratio,structured similarity indexing method value,and subjective perception satisfaction than numerous denoising approaches,which indicates that the approach has higher denoising efficiency and rich detail preservation.