The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). To date, it contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format).
The Lemon Leaf Disease Dataset (LLDD) is a high-quality image dataset designed for training and evaluating machine learning models for lemon leaf disease classification. The dataset contains 9 classes of images of healthy and diseased lemon leaves, such as; Anthracnose. Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites, making it suitable for tasks such as plant disease instance segmentation, detection, image classification, and deep learning applications in agriculture.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
Following the setup of previous works [8, 16], we conducted experiments on various bit image restoration tasks.
We utilized a dataset of 2000 16-bit images, with training
data sourced from SINTEL [37] and FIVE-K [38]. SINTEL
is an animated short film dataset containing over 20,000 16-
bit lossless images with a resolution of 436 × 1024 pixels. In
FIVE-K, randomly select images from 5,000 16-bit natural
images for the experiment.The test set includes 8 images
randomly chosen from the SINTEL dataset (referred to as
