Image Processing

Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.

Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.

This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Subjects with diabetes additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification into subgroups: diabetes only, diabetes with retinopathy, or diabetes with related complications such as hypertension.

This dataset selects the CIFAR-10 small-scale general object recognition dataset, which contains a total of 60,000 RGB color images with a size of 32×32, belonging to 10 categories. The dataset is divided into a training set of 40,000 images, a testing set of 10,000 images, and a validation set of 10,000 images. The images in the dataset are processed with bit masking, and the image classification results are compared with those of the original dataset, providing an experimental basis for the study of how bit masking improves the accuracy and speed of the algorithm.

‌DRIVE Dataset‌: The DRIVE dataset consists of 40 fundus images collected from 7 patients with retinal disease and 33 healthy individuals. Each image has a resolution of 584 × 565 pixels. A total of 20 images are used for training, while the remaining 20 images are designated for testing. Each image has two annotations provided by two different experts, and we use the first annotation as the ground truth.