Cartoon image and video up-sampling method based on gradient domain transformation

Abstract

The invention belongs to the field of image and video processing, and in particular relates to an image and video up-sampling method based on gradient domain transformation, which is particularly suitable for processing cartoon images and videos. The method comprises the following steps of: firstly carrying out up-sampling and amplification on a low-resolution image, and then obtaining the gradient of a high-resolution image after gradient transformation to be used as a constraint reconstructed high-resolution image according to a defined gradient domain transformation method. The method provided by the invention can rapidly and effectively carry out up-sampling on the cartoon images or videos, the computation is simple, the image after reconstruction has natural color, fuzzy edges caused by interpolation become clear, details are restored to a certain degree, and the visual effect is better.

Description

technical field The invention belongs to the field of image and video processing, in particular to an image and video upsampling method based on gradient domain transformation, and is especially suitable for processing cartoon images and videos. Background technique Image and video upsampling technology (hereinafter referred to as upsampling technology) is similar to the super-resolution technology of images and videos. It is expected to enlarge low-resolution images and videos, and keep the edges clear, without jagged and other phenomena, and has achieved improved visual effects. purpose. Up-sampling technology is widely used in video communication, object recognition, high-definition television, image compression and transmission and other fields. Currently, the upsampling methods used in commercial software are mainly interpolation algorithms, such as nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation. These methods are characterized by r...

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Problems solved by technology

The disadvantage is that the calculation is more complicated, and the reconstruction result is related to the training set, and the recovered details are also false, which only improves the image effect visually.

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