Entropy-based bilateral filtering with a new range kernel

Bilateral filter (BF) is a well-known edge-preserving image smoothing technique, which has been widely used in image denoising. The major drawback of BF is that its range kernel is sensitive to noise. To address this issue, we propose an entropy-based BF (EBF) with a new range kernel which contains a new range distance. The new range distance is robust to noise by exploiting the information from the denoised estimate and the corresponding method noise, i.e., the difference between the noisy image and its denoised estimate. Moreover, in order to consider the local statistics of images, local entropy is applied to adaptively guide the range parameter selections. This allows our method to adapt to the images with different characteristics. Experimental results demonstrate that the proposed EBF significantly outperforms the standard BF in terms of both quantitative metrics and subjective visual quality.