Image enhancement method based on all 1*1 convolution neural network

Abstract

The invention discloses an image enhancement method based on an all 1*1 convolution neural network. A 1*1 convolution neural network is constructed, and pixels in a local image block in a low-qualityimage or an entire image are rearranged randomly (Pixel shuffle), and that rearranged image blocks or images are used as inputs. Then the latent variables estimated by the network are processed. Basedon the imaging model corresponding to the latent variable, the mathematical expression of the clear image estimated from the low-quality image and the latent variable is obtained, and the enhanced result is calculated. Compared with the convolution kernel method which is widely used in traditional convolution neural networks, it achieves the purpose of maintaining the model representation abilitywith fewer parameters and less computational load, so as to estimate the potential variables in image enhancement quickly and accurately.

Description

technical field [0001] The invention belongs to the field of image processing, and designs an image enhancement method based on a full 1*1 convolutional neural network. Background technique [0002] The image enhancement problem in image processing is mostly an image inverse problem (Inverse Problem) or an ill-posed problem (Ill-posed Problem). Researchers have proposed many methods, including: heuristic methods based on image priors, regular constraint modeling based Optimization methods, machine learning based methods. These methods can be interpreted as modeling certain statistical characteristics of natural images, which can effectively solve latent variables in ill-conditioned problems, and then enhance images. However, modeling the statistical properties in natural images for efficient image enhancement remains a challenging task due to the complexity, diversity, and high-dimensional distribution of pixels. [0003] A feasible solution is to assume statistical prior ...

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This results in high computational cost, limiting its wide application

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