Scene segmentation-based landscape image processing method, device and computing device

Abstract

The invention discloses a landscape image processing method, device, computing device and computer storage medium based on scene segmentation. The method includes: acquiring the landscape image to be processed; The scene segmentation result corresponding to the landscape image; according to the scene segmentation result corresponding to the landscape image to be processed, the contour information of the specific object is determined; according to the contour information of the specific object, a beautification effect is added to the specific object to obtain the processed landscape image. The technical solution can quickly and accurately obtain the scene segmentation result corresponding to the landscape image, and based on the scene segmentation result, can more accurately add beautification effects to the sky, grassland and other scenery in the landscape image, and improve the picture display effect.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a scene segmentation-based landscape image processing method, device, computing device and computer storage medium. Background technique [0002] In the existing technology, image scene segmentation processing methods are mainly based on fully convolutional neural networks in deep learning. These processing methods use the idea of ​​transfer learning to migrate the network obtained through pre-training on large-scale classification data sets to image processing methods. The segmentation network is trained on the segmentation data set to obtain the segmentation network for scene segmentation, and then the segmentation network is used to segment the scene of the image. [0003] The network architecture used in the segmentation network obtained in the prior art directly uses the image classification network, and the size of the convolution block in the convolutional...

AI Technical Summary

Problems solved by technology

However, for image scene segmentation, the scene often contains objects of different sizes. Using a segmentation network with a fixed-size receptive field often causes problems when dealing with too large and too small objects. For example, for smaller objects, the receptive field It will capture too much background around the target, thereby confusing the target with the background, causing the target to be missed and misjudged as the background; for larger targets, the receptive field can only capture a part of the target, which makes the target category judgment biased, resulting in Discontinuous Segmentation Results

Therefore, the image scene segmentation processing method in the prior art has the problem of low accuracy of image scene segmentation, so the obtained segmentation results cannot be used to add processing effects to the sky, grass and other landscapes in the landscape image. The resulting processed landscape image displays poorly

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