This paper studies the performance of least mean square (LMS) adaptive filters for prewhitening noise and multiframe accumulation algorithm for the detection of point target in IR image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter and noise of much larger spatial extent, and the signal-to-noise ratio (SNR) is very low (SNR < 2). Traditional target detection algorithms involve background suppression/target enhancement that usually requires spatial and temporal prewhitening. In our scheme, adaptive Prediction Filters are employed as prewhitening filters to enhance the dim target and suppress the correlated background. According to the correlation of noise and clutter, 1D least mean square adaptive filters are adopted, which update filter weights based on the spatial coherence between the signal and noise components of the data. As a result of prewhitening the SNR has been increased a lot. In our scheme, the dim target moves very slowly (v < 0.5 pixel/frame), so an effective algorithm to enhance target is to sum up target energy through successive frames. Considering low speed of the target, summing operation may be operated by directly adding N consecutive frames under the assumption that the point target stays at a pixel at least in N frames. The summed-up result image is then processed by a proper threshold to pick out candidate target points with high summed energy. The above N-frame adding and thresholding procedure is recursively done as the image sequences are continually input from infrared sensor. All the thresholded result images are added up to form a new result image, in which the picked-out candidate target points embody target trajectories with continuous points, while noisy false targets with non- continuous points. In IR image, there are some stationary objects. To discriminate moving targets from almost stationary objects in background, the intensity time sequence on trajectories are used to extract their different intensity features.