A convolutional neural network model generation method and an image quality optimization method

Abstract

The invention discloses a convolutional neural network (CNN) model generation method. The method comprises the following steps: constructing a CNN model structure and setting a loss function expression; A plurality of training image pairs are acquired, each training image pair comprises an input image and a target image, and the input image and the target image are images obtained by shooting thesame scene through a mobile terminal and a single lens reflex; Each training image pair is registered, so that an input image included in the training image pair is aligned with the target image; Using the registered training image pair as a training sample to train a CNN model; and continuously updating model parameters of the CNN model, calculating a loss function value of the CNN model according to the loss function expression every time the model parameters are updated, and when the loss function value is converged, stopping the training process to obtain the model parameters of the CNN model so as to generate the CNN model. The invention also discloses a corresponding image quality optimization method and computing equipment.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for generating a convolutional neural network model for optimizing image quality, an image quality optimization method, and a computing device. Background technique [0002] With the development of technology, mobile terminals (such as mobile phones, tablet computers, etc.) have become commonly used camera devices for people, and their imaging effects are getting better and better. However, due to the large difference in photosensitive elements, the imaging effect of mobile terminals is still difficult to match that of SLR cameras, and there is a large gap with SLR images in terms of contrast, saturation, and fineness. In order to improve the imaging effect of the mobile terminal and improve the user's photographing experience, a possible method is to use an image processing algorithm to process the photographed image of the mobile terminal to optimize the quali...

AI Technical Summary

Problems solved by technology

However, the color palette style of this method is very uniform, not flexible enough to apply to multiple scenes

The development of Convolutional Neural Network (CNN, Convolutional Neural Network) has brought new ideas to image processing. However, the CNN-based algorithm requires strict alignment of training image pairs, making it difficult to apply to the conversion of mobile phone image quality to SLR image quality. in the scene

In addition, the existing related algorithms are not good enough to deal with the details, and there will be problems such as amplifying noise and losing details.

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