Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images
This addresses noise reduction in OCT images for medical imaging applications, but it appears incremental as it builds on existing decomposition techniques.
The paper tackled speckle noise in retinal OCT images by using a sparse and low-rank decomposition method for batch alignment and denoising, resulting in better performance compared to simple registration-based methods.
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decomposition based technique. Finally the denoised image is created by median filtering of the low-rank component of the processed data. Simultaneous decomposition and alignment of the images result in better performance in comparison to simple registration-based methods that are used in the literature for noise reduction of OCT images.