ABSTRACT Low‐quality fundus images pose significant challenges for both ophthalmologists and computer‐aided diagnosis systems. While many existing deep learning‐based image quality enhancement algorithms require low‐ and high‐quality image pairs for training, such pairs are often difficult to obtain in practice. On the other hand, unpaired image enhancement algorithms tend to struggle in preserving small structures and suppressing artefacts, which are crucial for medical applications. To address these issues, we propose an unpaired structure‐preserving cycle quality alternating network for low‐quality fundus image enhancement. Our method consists of three main components: (1) a cycle quality alternating framework to provide pixel‐wise supervision for unpaired image enhancement, (2) a quality‐aware disentangle module to enhance the extrinsic representation of the low‐quality image with the high‐quality reference image, and (3) an instance normalized skip to improve the network's structure‐preserving capability. We tested our method on both synthetic and authentic clinical images with pathological structures and found it to be superior to state‐of‐the‐art algorithms in terms of improving image quality while preserving delicate structures. Additionally, the proposed network demonstrated strong generalization ability in improving the quality of unseen images, as tested on 135‐degree neonatal fundus images.

Face clustering is a method for unlabeled image annotation and has attracted increasing attention. Existing methods have made significant breakthroughs by introducing Graph Convolutional Networks (GCNs) on the affinity graph. However, such graphs will contain many vertex pairs with inconsistent similarities and labels, thus degrading the model’s performance. There are already relevant efforts for this problem, but the information about features needs to be mined further. In this paper, we define a new concept called confidence edge and guide the construction of graphs. Furthermore, a novel confidence-GCN is proposed to cluster face images by deriving more confidence edges. Firstly, Local Information Fusion is advanced to obtain a more accurate similarity metric by considering the neighbors of vertices. Then Unsupervised Neighbor Determination is used to discard low-quality edges based on similarity differences. Moreover, we elaborate that the remaining edges retain the most beneficial information to demonstrate the validity. At last, the confidence-GCN takes the graph as the input and fully uses the confidence edges to complete the clustering. Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art. At the same time, comparable results are obtained on the fashion dataset.

Cataracts are the leading cause of visual impairment worldwide. Examination of the retina through cataracts using a fundus camera is challenging and error-prone due to degraded image quality. We sought to develop an algorithm to dehaze such images to support diagnosis by either ophthalmologists or computer-aided diagnosis systems. Based on the generative adversarial network (GAN) concept, we designed two neural networks: CataractSimGAN and CataractDehazeNet. CataractSimGAN was intended for the synthesis of cataract-like images through unpaired clear retinal images and cataract images. CataractDehazeNet was trained using pairs of synthesized cataract-like images and the corresponding clear images through supervised learning. With two networks trained independently, the number of hyper-parameters was reduced, leading to better performance. We collected 400 retinal images without cataracts and 400 hazy images from cataract patients as the training dataset. Fifty cataract images and the corresponding clear images from the same patients after surgery comprised the test dataset. The clear images after surgery were used for reference to evaluate the performance of our method. CataractDehazeNet was able to enhance the degraded image from cataract patients substantially and to visualize blood vessels and the optic disc, while actively suppressing the artifacts common in application of similar methods. Thus, we developed an algorithm to improve the quality of the retinal images acquired from cataract patients. We achieved high structure similarity and fidelity between processed images and images from the same patients after cataract surgery.