Abstract: An image enhancement processing method includes: acquiring an initial image, preprocessing the initial image, and acquiring an original feature image containing a target feature; performing an edge detection on the original feature image using an edge detection algorithm to obtain an original gradient image, obtaining a statistics ring based on the original feature image, and performing an iterative process on the statistics ring; obtaining a to-be-processed image based on an inner diameter of on the statistics ring, and determining to-be-processed parameters of the to-be-processed image: acquiring a standard image corresponding to the target feature, determining a standard area corresponding to the standard image, and acquiring standard image parameters corresponding to the standard area; performing a migration process on the to-be-processed image to obtain a migration image; and performing a restricted contrast adaptive histogram equalization process on the migration image to obtain a target enhanced image.

Abstract: An image enhancement processing method includes: acquiring an initial image, preprocessing the initial image, and acquiring an original feature image containing a target feature; performing an edge detection on the original feature image using an edge detection algorithm to obtain an original gradient image, obtaining a statistics ring based on the original feature image, and performing an iterative process on the statistics ring; obtaining a to-be-processed image based on an inner diameter of on the statistics ring, and determining to-be-processed parameters of the to-be-processed image: acquiring a standard image corresponding to the target feature, determining a standard area corresponding to the standard image, and acquiring standard image parameters corresponding to the standard area; performing a migration process on the to-be-processed image to obtain a migration image; and performing a restricted contrast adaptive histogram equalization process on the migration image to obtain a target enhanced image.

Abstract: A method and device for neural network-based optical coherence tomography (OCT) image lesion detection, and a medium are provided. The method includes the following. An OCT image is obtained. The OCT image is inputted into a lesion-detection network model. A position, a category score, and a positive score of each lesion box in the OCT image are outputted through the lesion-detection network model. A lesion detection result of the OCT image is obtained according to the position, the category score, and the positive score of each lesion box. The lesion-detection network model includes a category detection branch configured to obtain, for each of the anchor boxes, a position and a category score of the anchor box, and a lesion positive score regression branch configured to obtain, for each of the anchor boxes, a positive score of whether the anchor box belongs to a lesion, to reflect severity of lesion positive.