基于卷积字典扩散模型的眼底图像增强算法

Objective Retinal fundus images have important clinical applications in ophthalmology. These images can be used to screen and diagnose various ophthalmic diseases，such as diabetic retinopathy，macular degeneration，and glaucoma. However，the acquisition of these images is often affected by various factors in real scenarios，including lens defocus，poor ambient light conditions，patient eye movements，and camera performance. These issues often lead to quality problems such as blurriness，unclear details，and inevitable noise in fundus images. Such poor-quality images pose a challenge to ophthalmologists in their diagnostic work. For example，blurred images will lead to the absence of detailed information about the morphological structure of the retina，which causes difficulty for the physicians to accurately localize and identify abnormalities，lesions，exudations，and other conditions. Existing enhancement methods for fundus images have progressed in improving image quality. However，some problems still exist，such as image blurring，artifacts，missing high-frequency information，and increased noise. Therefore，in this study，we propose a convolutional dictionary diffusion model，which combines convolutional dictionary learning with conditional diffusion model. This algorithm aims to cope with the abovementioned problems of low-quality images to provide an effective tool for fundus image enhancement. Our approach can improve the quality of fundus images and enable physicians to increase diagnostic confidence，improve assessment accuracy，monitor treatment progress，and ensure better care for patients. This method will contribute to ophthalmic research and provide more opportunities for prospective healthcare management and medical intervention，which positively impacts patients’ocular health and overall quality of life. Method The algorithm consists of two parts：simulation of diffusion process and inverse denoising process. First，random noise is gradually added to the input image to obtain a purely noisy image. Then，a neural network is trained to gradually remove the noise from the image until a clear image is finally obtained. This study takes the blurred fundus image as the conditional information to better preserve the fine-grained structure of the image. Collecting blurred-clear fundus image pairs is difficult. Thus，synthetic fundus dataset is widely used for training. Therefore，a Gaussian filtering algorithm is designed to simulate the defocus blur images. In the training process，the conditional information and the noisy image are first spliced and fed into the network，and the abstract features of the image are extracted by continuously reducing the image size through downsampling. This procedure can significantly reduce the time and space complexity of the sparse representation calculation. Then，the convolutional network is used to implement convolutional dictionary learning and obtain the sparse representation of the image. Given that the self-attention mechanism can capture non-local similarity and long-range dependency，this study adds self-attention to the convolutional dictionary learning module to improve the reconstruction quality. Finally，hierarchical feature extraction is achieved by feature concatenation to realize information fusion between different levels and better use local features in the image. The downsampled feature is recovered to the original image size by an inverse convolutional layer. The model minimizes the negative log-likelihood loss，which represents the difference in probability distribution between the generated image and the original image. After the model is trained，a clear fundus image is generated by gradually removing the noise from a noisy picture with a blurred image as conditional input. Result The proposed method was evaluated on EyePACS dataset，and multiple experiments were performed on synthetic datasets DRIVE （digital retinal images for vessel extraction），CHASEDB1（child heart and health study in England），ROC（retinopathy online challenge），realistic datasets RF（real fundus）and HRF（high-resolution fundus）to demonstrate the generalizability of our model. Experimental results show that the evaluation metrics peak signal-to-noise ratio（PSNR）and learned perceptual image patch similarity（LPIPS）are improved on average by 1. 992 9 and 0. 028 9，respectively，compared with the original diffusion model（learning enhancement from degradation（Led））. Moreover，the proposed approach was used as a preprocessing module for downstream tasks. The experiment on retinal vessel segmentation is adopted to prove that our approach can benefit the downstream tasks in clinical application. The results of segmentation experiments on the DRIVE dataset show that all the segmentation metrics improve compared with the original diffusion model. Specifically，the area under the curve（AUC），accuracy （Acc），and sensitivity（Sen）are improved by 0. 031 4，0. 003 0，and 0. 073 8 on average，respectively. Conclusion The proposed method provides a practical tool for fundus image deblurring and a new perspective to improve the quality and accuracy of diagnostic. This approach has a positive impact on patients and ophthalmologists and is expected to promote further development in the interdisciplinary research of ophthalmology and computer science.