本文为印度Rourkela国立技术研究所（作者：Siba Prasad Tudu）的学士论文，共56页。
图像恢复是一种对已退化的图像进行恢复的过程，其前提条件是图像获取不能再次进行，或是重新获取图像的过程成本更高。我们可以通过预先知道噪声或干扰来恢复图像，这些干扰是导致图像退化的直接原因。图像恢复分为空间域和频域两个方面。在空间域中，恢复图像的过滤操作是通过直接操作数字图像的像素来完成的；在频域中，通过对图像进行傅立叶变换，将空间域映射到频域，完成滤波操作。通过将图像映射到频域，图像可以提供过滤操作的细节。滤波后，通过反傅立叶变换将图像重新映射到空间域中，得到恢复后的图像。
研究了不同的噪声模型。研究了空间域和频率域的不同滤波技术，并用matlab编写并仿真了改进算法。通过考虑峰值信噪比（PSNR）和均方误差（MSE），验证了不同算法的恢复效率。
Image restoration is the process of restoring degraded images which cannot be taken again or the process of obtaining the image again is costlier. We can restore the images by prior knowledge of the noise or the disturbance that causes the degradation in the image. Image restoration is done in two domains: spatial domain and frequency domain. In spatial domain the filtering action for restoring the images is done by directly operating on the pixels of the digital image. In frequency domain the filtering action is done by mapping the spatial domain into the frequency domain by taking fourier transform of the image function. By mapping the image into frequency domain an image can provide an insight for filtering operations. After the filtering, the image is remapped into spatial domain by inverse fourier transform to obtain the restored image. Different noise models were studied. Different filtering techniques in both spatial and frequency domains, were studied and improved algorithms were written and simulated using matlab. Restoration efficiency was checked by taking peak signal to noise ratio(psnr) and mean square error(mse) into considerations.
1 引言
2 空间域滤波
3 频率域滤波
4 仿真及结果
5 结论与未来工作展望
6 参考文献
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