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.

通过使用 T 形模型架构对低分辨率图像进行全局结构信息的捕捉，并逐步恢复细节，我们提出了一种轻量级的 DDPM（LighTDiff），该模型在保留性能的同时显著减小了模型大小。同时，我们引入了时间光单元（TLU）来实现稳定训练和改善去噪结果，通过与去噪图像特征建立时间步骤的关联，建立去噪步骤的时间依赖性。此外，我们还引入了色度平衡器（CB）来缓解扩散模型中可能存在的光谱变化问题。我们的 LighTDiff 在计算效率方面优于许多竞争的 LLIE 方法。

LighTDiff：使用 T-Diffusion 进行手术内窥镜图像低光增强

LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with  T-Diffusion

Access to high-quality datasets in the medical industry limits machine
learning model performance. To address this issue, we propose a Denoising
Diffusion Probabilistic Model (DDPM) combined with a UNet architecture for
X-ray image synthesis. Focused on pneumonia medical condition, our methodology
employs over 3000 pneumonia X-ray images obtained from Kaggle for training.
Results demonstrate the effectiveness of our approach, as the model
successfully generated realistic images with low Mean Squared Error (MSE). The
synthesized images showed distinct differences from non-pneumonia images,
highlighting the model's ability to capture key features of positive cases.
Beyond pneumonia, the applications of this synthesizer extend to various
medical conditions, provided an ample dataset is available. The capability to
produce high-quality images can potentially enhance machine learning models'
performance, aiding in more accurate and efficient medical diagnoses. This
innovative DDPM-based X-ray photo synthesizer presents a promising avenue for
addressing the scarcity of positive medical image datasets, paving the way for
improved medical image analysis and diagnosis in the healthcare industry.

提出了一种结合 Denoising Diffusion Probabilistic Model (DDPM) 和 UNet 架构的 X-ray 图像合成方法，通过使用 3000 多张从 Kaggle 获取的肺炎 X-ray 图像进行训练，成功地生成了与非肺炎图像有明显差异的逼真图像，突出了模型捕捉正样本的关键特征的能力，该合成器能够生成高质量图像，有助于提高机器学习模型的性能，为医疗行业中的更准确、高效的医学诊断铺平道路。