Image enhancement method using target tracking sequence

Abstract

According to the image enhancement method using the target tracking sequence provided by the invention, the to-be-labeled image frame is detected through the target detection model to obtain the interested target, so that useful information in the to-be-labeled image frame can be determined and highlighted. And all interested targets are tracked through a target tracking algorithm, so that each interested target in the same category can be distinguished, missing detection of target detection can be prevented, and the detection accuracy is improved. In addition, due to the fact that the image frame to be marked is cut according to the tracking frame, useless information is removed, only interested target information is reserved, computing power is concentrated on an interested target, computing consumption is saved, a low video memory is occupied, and the neural network speed is increased. And the image enhancement is performed on the to-be-enhanced image sequence through the image enhancement model, so that fusion can be performed according to time and space information between front and back frames, and the image enhancement effect is enhanced.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to an image enhancement method using a target tracking sequence. Background technique [0002] Image enhancement refers to highlighting the useful information in the image and removing or weakening the useless information according to a specific requirement, thereby making the original unclear image clear or emphasizing certain interesting features and expanding the characteristics of different objects in the image. The difference between them can suppress uninteresting features, improve image quality, enrich information, enhance image interpretation and recognition effects, and meet the needs of some special analysis. At the same time, the image is blurred due to camera shake, object movement, and lens out of focus, so image enhancement is required for super-resolution restoration, deblurring, and other operations. Among them, image super-resolution (SR) refers to recove...

AI Technical Summary

Problems solved by technology

Although the above operations have improved the detection or recognition accuracy of the neural network to a certain extent, the non-target areas in the entire enhanced image input tend to scatter computing power, waste computing resources, occupy too much video memory, and cause excessive memory. Expenses, unstable detection results or recognition results, which affect the speed and accuracy of the neural network, and in the above operations, the training image or the image to be tested has a higher resolution requirement, resulting in low-resolution images that cannot be input to the neural network , so that the use of neural networks has certain limitations

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