近紅外線影像之非均勻校正與壞點修復

Abstract

本論文使用非均勻校正(NUC)與壞點修正演算法修正近紅外線影像。在非均勻現象上，我們採用了2點式校正與最小平均平方法，此2種方法分別為非均勻校正中主要使用的2大類:基於參考平面與基於環境2種方式，兩點校正是一個高度精確的方法，不幸的是，他需要精密的儀器來測量參考圖像；最小平均平方法不需使用參考圖像，但演算法之速度難以使用在即時影像中，我們將測試並分析2種演算法。 為了提高壞點校正效能，我們使用改進基於同儕濾波器(peer group filter)之壞點修正法。在紅外線焦平面探测器所偵測影像中，經常出現團狀之壞點，我們必須執行一種特別的影像校正法。我們採用可自動調整濾波器遮罩大小來修正團狀之壞點。我們預設使用之濾波器遮罩大小為3×3之遮罩，可以盡可能的保持圖像清晰度；在遇到無法修正之壞點時，濾波器遮罩之大小將會自動增加，以提高修正能力。通過這項計劃，它是更準確地找出壞像素，將由同儕濾波器的中值取代壞點。

In this thesis, we use non-uniformity correction (NUC) and bad pixel correction to correct infrared image of a NIR sensor. We employ Two-point calibration and Least Mean Squares method, there are mainly used two categories of the calibration for non-uniformity correction, reference-based and scene-based correction algorithm. Two-point correction is a highly accurate method, unfortunately, it needs sophisticated instruments to measure the reference image; LMS method only need the readout infrared data captured by the imaging system and compensate the non-uniform response of pixels during its normal operation, But the speed of the algorithm is difficult to use in real-time video. We will test and analyze the two kinds of algorithms. For bad pixel correction, in order to improve the performance of bad pixel correction, we have improved bad pixel correction which is based on the peer filter. Because bad pixels in the infrared images which are detected by infrared focal plane array sensor are frequently in blobs than kinds of images, we must implement a specific method of image correction. We employ adjustable window size that can increase the window automatically where bad pixels are in blobs. We use 3×3 window as default working window for sharpness maintenance, if the small window does not correct a bad pixel, the window size will increase automatically to enhance the correction capability. By this scheme, it is more accurate to locate bad pixel, and bad pixels will be replaced by the median of the peer group.