Image enhancement method, apparatus, terminal device, and storage medium

Abstract

The invention discloses an image enhancement method, which relates to the artificial neural network field. The method comprises the following steps: acquiring an original image; inputting the originalimage into a preset image enhancement network, and performing step-by-step enhancement on the original image to obtain an enhanced image corresponding to the original image, wherein the image enhancement network comprises at least two levels of sub-networks, the original image is used as input to a first sub-network of the image enhancement network, the first-level sub-network outputs a first-level node image, the node image output by each-level sub-network is used as an input of a next-level sub-network, the last-level sub-network of the image enhancement network outputs a last-level node image, and the last-level node image is used as the enhancement image. The first-level sub-network outputs a first-level node image, and the node image output by each-level sub-network is used as an input of a next-level sub-network. The image enhancement method provided by the invention can effectively improve the convergence of image enhancement operation, reduce the amount of operation required for image enhancement, and improve the operation efficiency and stability of image enhancement.

Description

technical field [0001] The present invention relates to the field of artificial neural networks, in particular to an image enhancement method, device, terminal equipment and storage medium. Background technique [0002] Image enhancement is the enhancement of image visual effects, including the improvement of image clarity, resolution, and signal-to-noise ratio. Image enhancement often needs to extract features from the original image to enhance the original image. Therefore, the neural network that processes images based on features has become an indispensable tool in image enhancement. [0003] In the prior art, the original image is usually input into a single-stage convolutional neural network, and the enhanced image corresponding to the original image is directly calculated by the single-stage convolutional neural network, and the enhanced image is output. [0004] However, in the process of realizing the present invention, the inventors found that since the prior art ...

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Problems solved by technology

[0004] However, in the process of realizing the present invention, the inventors found that since the prior art uses an image enhancement method that directly calculates the corresponding enhanced image based on the original image, due to poor convergence, it often requires a huge amount of time when performing image enhancement operations. The amount of calculation is also accompanied by the adverse consequences of poor stability, especially when performing image enhancement tasks with a large span. result

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