  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.
