红外焦平面探测器输出的模拟信号通常采用14 bit AD进行数字化,并进行后续处理,而常用的显示设备只能显示8 bit图像,于是最终显示需要对图像进行压缩,压缩过程直接影响显示效果。与之相关的图像细节增强和动态范围压缩技术亦是当前行业...红外焦平面探测器输出的模拟信号通常采用14 bit AD进行数字化,并进行后续处理,而常用的显示设备只能显示8 bit图像,于是最终显示需要对图像进行压缩,压缩过程直接影响显示效果。与之相关的图像细节增强和动态范围压缩技术亦是当前行业内重点研究的技术。基于已提出的一种细节增强和动态压缩算法,在以Xilinx公司的XC5VLX50T FPGA为核心处理器件的图像处理板上对算法进行了工程实现,算法完全在FPGA片内利用Verilog-HDL编写实现,不占用片外资源,片内占用资源适中,处理延时小于200μs。实际观测试验验证了算法以及实现手段的有效性。展开更多
Based on the analysis to the behavior of bad pixels, a statistics-based auto-detecting and compensation algorithm for bad pixels is proposed. The correcting process is divided into two stages： bad pixel detection and...Based on the analysis to the behavior of bad pixels, a statistics-based auto-detecting and compensation algorithm for bad pixels is proposed. The correcting process is divided into two stages： bad pixel detection and bad pixel compensation. The proposed detection algorithm is a combination of median filtering and statistic method. Single frame median filtering is used to locate approximate map, then statistic method and threshold value is used to get the accurate location map of bad pixels. When the bad pixel detection is done, neighboring pixel replacement algorithm is used to compensate them in real-time. The effectiveness of this approach is test- ed by applying it to I-IgCATe infrared video. Experiments on real infrared imaging sequences demonstrate that the proposed algorithm requires only a few frames to obtain high quality corrections. It is easy to combine with traditional static methods, update the pre-defined location map in real-time.展开更多
文摘红外焦平面探测器输出的模拟信号通常采用14 bit AD进行数字化,并进行后续处理,而常用的显示设备只能显示8 bit图像,于是最终显示需要对图像进行压缩,压缩过程直接影响显示效果。与之相关的图像细节增强和动态范围压缩技术亦是当前行业内重点研究的技术。基于已提出的一种细节增强和动态压缩算法,在以Xilinx公司的XC5VLX50T FPGA为核心处理器件的图像处理板上对算法进行了工程实现,算法完全在FPGA片内利用Verilog-HDL编写实现,不占用片外资源,片内占用资源适中,处理延时小于200μs。实际观测试验验证了算法以及实现手段的有效性。
基金Sponsored by the National Natural Science Foundation of China(60877060)
文摘Based on the analysis to the behavior of bad pixels, a statistics-based auto-detecting and compensation algorithm for bad pixels is proposed. The correcting process is divided into two stages： bad pixel detection and bad pixel compensation. The proposed detection algorithm is a combination of median filtering and statistic method. Single frame median filtering is used to locate approximate map, then statistic method and threshold value is used to get the accurate location map of bad pixels. When the bad pixel detection is done, neighboring pixel replacement algorithm is used to compensate them in real-time. The effectiveness of this approach is test- ed by applying it to I-IgCATe infrared video. Experiments on real infrared imaging sequences demonstrate that the proposed algorithm requires only a few frames to obtain high quality corrections. It is easy to combine with traditional static methods, update the pre-defined location map in real-time.
