JIAN-JIUN DINGLiao, Chun LinChun LinLiao2023-06-062023-06-062023-01-0197815106630840277786Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/631817It is inevitable that an image is suffered from the noise. There are several existing image denoising algorithms. Some image denoising methods, including the Wiener filter, the maximum likelihood method, and their adaptive forms, can be implemented efficiently by the linear convolution and the two-dimensional discrete Fourier transform (2D DFT). However, their performance is highly affected by the distribution of the noise. For different values of the peak signal-to-noise ratio (PSNR) and different style of the noise, the optimal parameters for image denoising are also different. Therefore, noise estimation plays a critical role in image denoising. In this work, an effective way for noise estimation is proposed. The proposed algorithm is based on the following two priors. We think that, for an image without noise, there should be some patch that has the mean gradient near to zero. We call it the smooth patch prior. Moreover, in the frequency domain, there should be some bands that has the average energy near to zero. We call it the zero-band prior. Therefore, the noise can be estimated from the patch with a relatively smaller average gradient and its sparse part in the 2D DFT domain. With the proposed algorithm, the noise distribution can be well predicted and the optimal parameter of the image denoising filter can be determined automatically.denoising | image reconstruction | Noise estimation | sparse time-frequency domain[SDGs]SDG7conference paper10.1117/12.26670362-s2.0-85159359097https://api.elsevier.com/content/abstract/scopus_id/85159359097