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Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases — in contrast to their magnitude — reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels. [ABSTRACT FROM AUTHOR]

This study presents an image denoising technique using multiscale non‐local means (NLM) filtering combined with hard thresholding in curvelet domain. The inevitable ringing artefacts in the reconstructed image – due to thresholding – is further processed using a guided image filter for better preservation of local structures like edges, textures and small details. The authors decomposed the image into three different curvelet scales including the approximation and the fine scale. The low‐frequency noise in the approximation sub‐band and the edges with small textural details in the fine scale are processed independently using a multiscale NLM filter. On the other hand, the hard thresholding in the remaining coarser scale is applied to separate the signal and the noise subspace. Experimental results on both greyscale and colour images indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]