A denoising method comprising: (a) primarily denoising an input image through pixel-wise wiener filtering in a spatial domain (S110, S120); (b) dividing the input image into blocks by performing wavelet transform on the primarily denoised input image and then secondarily denoising the divided input image by applying different thresholds to the divided input image depending on the division level of the divided input image (S130, S140); and performing inverse wavelet transform, wherein (b) comprises: dividing the primarily denoised input image with a division level of N by performing waveform transform; and secondarily denoising the divided input image by applying different thresholds to the divided input image depending on the division level of the divided input image, and wherein in, if sub-blocks are not larger than round(N/2), and the absolute values of wavelet coefficients of sub-blocks are smaller than a reference value for a predetermined level, the wavelet coefficients of the sub-blocks are set to a value of 0, and if the sub-blocks are not larger than round(N/2), but the absolute values of the wavelet coefficients of the sub-blocks are not smaller than the reference value for the predetermined level, results of subtracting the reference value from the absolute values of the wavelet coefficients of the sub-blocks are output with signs of the wavelet coefficients of the sub-blocks kept intact, and wherein the reference value is calculated based on a median value, which is obtained by detecting a maximum among wavelet coefficients averages of only high-frequency blocks of the image in each of a plurality of rows of the image, which are high frequency components, and then averaging such maximums respectively detected from the plurality of rows of the image in consideration that the input image has already been primarily denoised through pixel-wise wiener filtering, by using the following equation: t = γ ⋅ σ ⋅ 2 ⁢ log ⁢ n / n where t denotes the reference point, n denotes a division level, σ denotes a noise level, and γ denotes the median value,and wherein: (a) comprises calculating the level of noise included in the input image; and comprises: dividing the input image into blocks with a division level of 1 by performing wavelet transform; dividing some of the blocks of the input image whose horizontal and vertical components are all high frequency components into sub-blocks; calculating an average of wavelet coefficients of the sub-blocks; and determining some of the sub-blocks having a wavelet coefficient of about 0 as sub-blocks where only noise exists and calculating the noise level by obtaining a square root of an average of variances of the corresponding sub-blocks where only noise exists. (a) primarily denoising an input image through pixel-wise wiener filtering in a spatial domain (S110, S120); (b) dividing the input image into blocks by performing wavelet transform on the primarily denoised input image and then secondarily denoising the divided input image by applying different thresholds to the divided input image depending on the division level of the divided input image (S130, S140); and dividing the primarily denoised input image with a division level of N by performing waveform transform; and secondarily denoising the divided input image by applying different thresholds to the divided input image depending on the division level of the divided input image, (a) comprises calculating the level of noise included in the input image; and comprises: dividing the input image into blocks with a division level of 1 by performing wavelet transform; dividing some of the blocks of the input image whose horizontal and vertical components are all high frequency components into sub-blocks; calculating an average of wavelet coefficients of the sub-blocks; and determining some of the sub-blocks having a wavelet coefficient of about 0 as sub-blocks where only noise exists and calculating the noise level by obtaining a square root of an average of variances of the corresponding sub-blocks where only noise exists. dividing the input image into blocks with a division level of 1 by performing wavelet transform; dividing some of the blocks of the input image whose horizontal and vertical components are all high frequency components into sub-blocks; calculating an average of wavelet coefficients of the sub-blocks; and determining some of the sub-blocks having a wavelet coefficient of about 0 as sub-blocks where only noise exists and calculating the noise level by obtaining a square root of an average of variances of the corresponding sub-blocks where only noise exists.