LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion
This work addresses a domain-specific challenge in medical imaging by providing a more practical solution for low-light enhancement in endoscopic surgeries, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of low-light image enhancement in surgical endoscopy by proposing LighTDiff, a lightweight diffusion model that achieves state-of-the-art performance with improved computational efficiency, reducing model size while maintaining effectiveness.
Advances in endoscopy use in surgeries face challenges like inadequate lighting. Deep learning, notably the Denoising Diffusion Probabilistic Model (DDPM), holds promise for low-light image enhancement in the medical field. However, DDPMs are computationally demanding and slow, limiting their practical medical applications. To bridge this gap, we propose a lightweight DDPM, dubbed LighTDiff. It adopts a T-shape model architecture to capture global structural information using low-resolution images and gradually recover the details in subsequent denoising steps. We further prone the model to significantly reduce the model size while retaining performance. While discarding certain downsampling operations to save parameters leads to instability and low efficiency in convergence during the training, we introduce a Temporal Light Unit (TLU), a plug-and-play module, for more stable training and better performance. TLU associates time steps with denoised image features, establishing temporal dependencies of the denoising steps and improving denoising outcomes. Moreover, while recovering images using the diffusion model, potential spectral shifts were noted. We further introduce a Chroma Balancer (CB) to mitigate this issue. Our LighTDiff outperforms many competitive LLIE methods with exceptional computational efficiency.