Real-time visualization of low contrast targets from high-dynamic range infrared images based on temporal digital detail enhancement filter

JOURNAL OF ELECTRONIC IMAGING(2015)

引用 8|浏览18
暂无评分
摘要
An image detail enhancement method to effectively visualize low contrast targets in high-dynamic range (HDR) infrared (IR) images is presented regardless of the dynamic range width. In general, high temperature dynamics from real-world scenes used to be encoded in a 12 or 14 bits IR image. However, the limitations of the human visual perception, from which no more than 128 shades of gray are distinguishable, and the 8-bit working range of common display devices make necessary an effective 12/14 bits HDR mapping into the 8-bit data representation. To do so, we propose to independently treat the base and detail image components that result from splitting the IR image using two dedicated guided filters. We also introduce a plausibility mask from which those regions that are prominent to present noise are accurately defined to be explicitly tackled to avoid noise amplification. The final 8-bit data representation results from the combination of the processed detail and base image components and its mapping to the 8-bit domain using an adaptive histogram-based projection approach. The limits of the histogram are accommodated through time in order to avoid global brightness fluctuations between frames. The experimental evaluation shows that the proposed noise-aware approach preserves low contrast details with an overall contrast enhancement of the image. A comparison with widely used HDR mapping approaches and runtime analysis is also provided. Furthermore, the proposed mathematical formulation enables a real-time adjustment of the global contrast and brightness, letting the operator adapt to the visualization display device without nondesirable artifacts. (C) 2015 SPIE and IS&T
更多
查看译文
关键词
detail enhancement,noise removal,time filtering,contrast enhancement,high-dynamic-range,low-dynamic-range,infrared images,human perception
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要