Retracted: Mimic Deep Learning Technique for Retinal Images Denoising in IoT based Medical Devices

Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

“…Similarly, Aurangzeb et al [19] improved the performance of supervised and unsupervised machine learning models for retinal blood vessel segmentation by applying improved particle swarm optimization to the CLAHE parameters, with a special focus on optimizing clip limits and context regions. In terms of noise removal, Rana et al [20] developed a sparse perceptual noise removal method that utilizes a convolutional neural network to remove noise, thereby improving retinal image recovery. The successful application of these methods further demonstrates the superior performance and adaptability of deep learning in the field of image enhancement.…”

“…In recent years, research for symptomatic vitreous opacity image enhancement has made significant progress in several areas. These studies typically use traditional methods [11][12][13][14][15] and deep learning methods [16][17][18][19][20] to improve the quality and clarity of symptomatic vitreous opacity images. Some studies use traditional methods, such as filtering, to improve the quality of symptomatic vitreous opacity images.…”

“…Although conventional methods perform well in some aspects, they may not be able to fully utilize the advantages of advanced techniques such as deep learning. In the case of deep learning methods [16][17][18][19][20], despite significant progress in image enhancement, a large amount of labeled data is usually required to train the model. Especially for symptomatic vitreous opacity image enhancement, obtaining a sufficient amount of labeling data can be a challenging task.…”