Blind estimation of white Gaussian noise variance in highly textured images

Abstract

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.

Efficient image noise estimation based on skewness invariance and adaptive noise injection

“…In our experiments, we compared the proposed method with many state‐of‐the‐art methods, such as those by Pyatykh et al [13], Liu et al [14], Ponomarenko et al [18], Zoran and Weiss [19], Lyu et al [21], and Dong et al [23]. All methods were evaluated using the following three image datasets: (i) all 24 images from the Kodak image database [29] of size 768 × 512 or 512 × 768, (ii) 100 images randomly selected from the TAMPERE17 image database [18] of size 512 × 512, and (iii) 100 images randomly selected from the UCID image database [30] of size 512 × 384 or 384 × 512.…”

“…The estimation performance comparisons of the proposed method and the comparative methods [13, 14, 18, 19, 21, 23] are shown in Table 2 and Fig. 7.…”

“…Zlokolica et al [17] proposed a wavelet‐based noise estimation method by analysing the distributions of spatial–temporal gradients from the finest scales of the spatial–temporal wavelet transform. Ponomarenko et al [18] proposed a noise variance estimation method for a high‐texture image, where the noise level was calculated by choosing the least energy coefficient after the DCT onto the input image. This type of method has good performance for noise estimation; however, its computational complexity is relatively high.…”

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

“…Transform-based methods perform the image analysis in a domain that offers a higher degree of separability between the noise signal and underlying image signal [29][30][31][32]. The choice of transformation and the associated domain of signal analysis are crucial factors in the performance of transform-based noise level estimation algorithms.…”

Adaptive Block-Based Approach to Image Noise Level Estimation in the SVD Domain

“…Since the filter-based noise level estimation algorithms are usually associated with the higher levels of accuracy and robustness, whilst the block-based algorithms are associated with simplicity and a lower computational load, many hybrid algorithms have been proposed in an attempt to combine the benefits of both approaches [19][20][21]. Transform-based methods transform the image from a spatial domain into another domain prior to noise level estimation [22][23][24][25]. Wavelet transform [23] and DCT transform [25] are popular choices of transform for noise level estimation.…”