Image bit enhancement method based on deep learning

Abstract

The invention discloses an image bit depth enhancement method based on a deep learning network, and the method comprises the following steps: carrying out the preprocessing of a high-bit lossless-image-quality image, and carrying out the quantification of the high-bit lossless-image-quality image into a low-bit image; designing a convolutional neural network based on deep learning, taking the quantized low-bit image as input, and taking perception loss between an output result and the original high-bit image as a loss function; training parameters of each convolution layer and each batch normalization layer in the model through an optimizer gradient descent loss function, and storing the corresponding model and parameters of all layers in the model after the descent amplitude of the modelloss function does not exceed a threshold value; and after the high-bit image is quantized into low bits, recovering the high-bit image through the stored convolutional neural network. According to the method, a convolutional neural network framework in deep learning is utilized, so that a high-quality high-bit image can be accurately recovered.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to an image bit enhancement method based on deep learning. Background technique [0002] With the development of science and technology, while people's material and cultural level is constantly improving, the requirements for the visual quality provided by the display are also constantly improving. People gradually hope that the display can provide a picture with higher definition and color closer to the real scene. Under such circumstances, high-definition displays and HDR (High Dynamic Range, High Dynamic Range) displays gradually occupy a larger market share by virtue of their excellent visual experience. [0003] However, most of the existing pictures and video data are shot and stored in low-bit form. Each color channel of each pixel in most images and videos is stored with 8 bits, so each color channel can represent up to 256 colors. Some network cameras even use 5, 6, and 5 bi...

AI Technical Summary

Problems solved by technology

[0006] At present, many algorithms for image bit depth enhancement based on simple calculations have been proposed, but most of the algorithms cannot solve the problem of image false contours well.

Some algorithms are calculated based on interpolation, which can eliminate false contours to a large extent, but generally cause image details and light contours to be blurred, especially in the LMM region (local maximum and minimum region, Local Maximum / Minimum Region )

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