A Multilevel Shrinkage Approach for Curvelet Denoising

TLDR: A scale adaptive threshold design for curvelet denoising where at each scale of curvelet transform a different threshold is applied to the transform coefficients to restore a noise free image is proposed.

Abstract: This paper suggests an image restoration technique when the image is corrupted by additive white Gaussian noise. Based on the fact that the discrete curvelet transform is redundant, it proposes a scale adaptive threshold design for curvelet denoising where at each scale of curvelet transform a different threshold is applied to the transform coefficients to restore a noise free image. The strategy is to generate a set of thresholds corresponding to the various subbands of the transform whereas the traditional soft/hard thresholding applies the same threshold to each scale of transform coefficients. It is demonstrated numerically that this scheme obtains comparable performance to the state-of-the-art denoising approaches for a wide range of noise levels. Due to the adaptive support, the edges are clean and the restored images are visually pleasant.