Unsupervised Region-Based Denoising for Optical Coherence Tomography Framework

Optical Coherence Tomography (OCT) is an emerging imaging tool that is now widely adopted in various medical settings such as cardiology and ophthalmology and is emerging in dentistry. In OCT, light of low coherence is used for image capturing which results in the introduction of speckle noise. Specifically, a degraded signal-to-noise ratio accentuates ambiguity in feature-extraction and contributes to the introduction of artefacts. This ultimately impacts its clinical utility where clear diagnostic detail is essential. In this work a concentrated-unsupervised deep learning denoising framework for OCT images is proposed, incorporating attention gate encoders. Attention gates are utilized to ensure focus on denoising the foreground and to ‘hard-threshold’ the background. Training data was created by processing the image with state-of-the-art denoisers (BM3D and NLM, etc.) to emphasize only essential data removal. The proposed framework was analysed quantitatively and visually, in comparison against state-of-the-art denoising algorithms. The experimental results show that the approaches verifiably remove speckle noise and achieves superior quality to well-known denoisers. The method improved the PSNR by 29.6 dB, CNR by 11.5 dB and, ENL by 1196.6 dB compared to original image and state-of-the-art denoisers.