红外焦平面成像质量受材料生长及器件制备工艺的影响,易出现盲元、条纹噪声等缺陷。条纹噪声经常会导致盲元的检测偏差,准确的盲元检测对于后续图像处理具有重要意义。利用双密度双树复数小波分解的多方向性小波系数,结合广义高斯分布...红外焦平面成像质量受材料生长及器件制备工艺的影响,易出现盲元、条纹噪声等缺陷。条纹噪声经常会导致盲元的检测偏差,准确的盲元检测对于后续图像处理具有重要意义。利用双密度双树复数小波分解的多方向性小波系数,结合广义高斯分布将高频小波系数按照对条纹噪声影响程度分别赋予不同权值并进行单支重构,消除了条纹噪声对盲元检测的影响,得到初步"干净"的预处理图像,进而对预处理图像运用3σ准则进行盲元检测。通过短波Hg Cd Te红外焦平面成像的实践验证,该方法对具有条纹噪声特征的红外图像盲元检测更加准确。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
文摘红外焦平面成像质量受材料生长及器件制备工艺的影响,易出现盲元、条纹噪声等缺陷。条纹噪声经常会导致盲元的检测偏差,准确的盲元检测对于后续图像处理具有重要意义。利用双密度双树复数小波分解的多方向性小波系数,结合广义高斯分布将高频小波系数按照对条纹噪声影响程度分别赋予不同权值并进行单支重构,消除了条纹噪声对盲元检测的影响,得到初步"干净"的预处理图像,进而对预处理图像运用3σ准则进行盲元检测。通过短波Hg Cd Te红外焦平面成像的实践验证,该方法对具有条纹噪声特征的红外图像盲元检测更加准确。
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.
