摘要:为了提高图像复原算法的性能 ,提出了一种改进的奇异值分解法估计图像的点扩散函数。从图像的退化离散模型 出发 ,对图像进行逐层分块奇异值分解 ,并自动选取奇异值重组阶数以减少噪声对估计的影响。利用理想图像奇异值向 量平均能谱指数模型 ,估计点扩散函数奇异值向量的频谱 ,再反傅里叶变换得到其时域结果。实验结果表明 ,该方法能 在不同信噪比情况下估计成像系统的点扩散函数 ,估计结果比原有估计方法有所提高 ,有望为图像复原算法的预处理提 供一种有效的手段。-Abstract : T

A 16-QAM signal X, whose power is normalized as unity, is transmitted with OFDM over the discrete-time channel model h which has been used in Homework #2 and #3. As depicted in the below figure, the transmitter (TX) is now equipped with an N-point ID

based on DFT algorithm, estimate the frequency of signal with different DFT size and SNR