LAUSR

Optical coherence tomography (OCT) is an emerging optical imaging modality with high resolution and non-invasive, which plays an important role in applications such as material detection and disease diagnosis, especially for ophthalmic retinal diseases such as age-related macular degeneration, diabetic macular edema and choroidal neovascularization. However, since OCT utilizes the coherent interference of light, the generated image is inevitably affected by speckle noise, which blurs the structural information of the image such as layer structure and lesion point, and the low-quality OCT image makes its subsequent application become difficult. To solve this problem, an OCT image denoising fusion based on discrete wavelet transform and spatial domain feature weighting is proposed in this paper. Extensibility experiments show that the proposed algorithm can better remove noise and retain its precise structural information compared with several state-of-the-art OCT image denoising algorithms.