Noise reduction for video signals

Noise reduction is an important aspect of video processing. This thesis proposes a motion adaptive temporal-spatial noise reduction system. The three-step motion detection algorithm is the key for the system. First the pixel-to-pixel difference between two frames are compared to a threshold. This step gives a preliminary motion detection result for each pixel in the target frame. The preliminary motion detection results then go through the impulse pattern recognition module, where some false detections are corrected. The last step of the motion detection method is the spatial motion detection. It corrects those motion pixels that were mis-detected as still pixels in previous steps. Therefore, the pixel accurate motion detection results between the target frame and a reference frame is obtained. These results are used to control the operations of the temporal filter and the spatial filter. The temporal average filter only operates on the pixels that is not in motion to avoid motion artifacts. The edge adaptive spatial filter does little spatial filtering in the still areas of the picture to maintain the spatial resolution, while does more aggressive filtering in motion areas of the picture because the human eye is less sensitive to the blurring of moving objects. Therefore, the maximum noise reduction effect is achieved. The proposed algorithm is implemented both in a hardware prototype using FPGAs and as a real-time software module on a VLIW DSP chip. Both prototypes made the intensive subjective evaluations of the algorithm possible. It is proved that the algorithm improves the picture quality about 6 dB, in terms of signal-tonoise ratio and subjective estimation, for most of the noisy pictures. It introduces minimum artifacts.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat