background clutter

基本解釋

The effective detection for point target in low SNR IR images is dependent greatly on the suppression of background clutter.
低信噪比條件下點目標的檢測性能在很大程度上依賴于對紅外背景雜波的抑制情況。
The test experiments with real infrared date illustrate the effectiveness of the proposed background clutter suppression algorithm.
利用幾組實測數(shù)據(jù)的測試實驗結(jié)果表明了所提出的算法的有效性。
The experiment results indicate that wavelet transform can enhance the target and suppress background clutter effectively, and improve the target detection probability and reduce the detection error.
實驗結(jié)果表明，小波變換能很好地增強目標，抑制背景雜波，從而提高目標檢測概率，降低誤檢測。

背景雜波
Firstly,DTCWT is adopted to decompose the input infrared image,which extracts multi-scale detail features of images. Then,according to difference between targets and background clutter signals,the singular value decomposition is introduced to process sub-bands,and maximum eigenvalues are utilized to compose the sub-bands.
首先采用DTCWT對圖像進行正變換,獲得圖像的多尺度和方向細節(jié)特征;然后根據(jù)目標和背景雜波信號系數(shù)在不同尺度之間的差異,對各子帶采用奇異值分解進行處理,并利用最大的特征值重構(gòu)子帶;最后將系數(shù)調(diào)整后的各子帶逆變換到圖像域,從而將弱小目標和背景雜波分離,達到抑制背景的目的。
背景混淆

背景雜波
The dim target usually hides in the constructed background clutter and heavy noise environment. In recent years, researchers pay more and more attention on the detection and tracking of dim target in IR image sequence.
遠距離的紅外成像目標通常隱藏在高度結(jié)構(gòu)化的背景雜波和強噪聲環(huán)境中,近些年來,強雜波條件下的可見光和紅外小目標的檢測研究工作已愈來愈為人們所重視。
雜波背景
Constant False Alarm Rate (CFAR) processing of SAR images facilitates target detection in spatially varying background clutter.
恒虛警（CFAR）處理是適用于不同雜波背景的目標檢測的一項重要技術(shù)。
背景雜斑