Dual-tree Complex Wavelet Transform based denoising for Random Spray image enahcement methods

摘要

This work introduces a novel way to reduce point-wise noise introduced or exacerbated by image enhancement methods leveraging the Random Spray sampling approach. Due to the nature of the spray, the sampling structure used, output images for such methods tend to exhibit noise with unknown distribution. The proposed noise reduction method is based on the assumption that the non-enhanced image is either free of noise or contaminated by non-perceivable levels of noise. The dual-tree complex wavelet transform is applied to the luma channel of both the non-enhanced and enhanced image. The standard deviation of the energy for the non-ehanced image across the six orientations is computed and normalized. The normalized map obtained is used to shrink the real coefficients of the enhanced image decomposition. A noise reduced version of the enhanced version can then be computed via the inverse transform. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach.