Research On Color Algorithm Of Gray Image Based On Deep Learning

Image colorization technology has a broad application prospect in many aspects,such as sketch picture coloring,black-and-white photo colorization and color processing of animation works.However,because the traditional coloring method needs to mark color lines on the grayscale target image artificially or select color reference images similar to the target image,manual intervention must be carried out in the process of image processing.Under the background of increasing quantization and complexity of image data which need to be colored,the traditional coloring methods which need manual intervention are more and more limited.Therefore,this paper uses the deep learning technology with excellent performance in large-scale data modeling for reference,applies it to the image color technology,and focuses on the automatic color algorithm of gray image.Using the powerful learning ability of convolutional neural network and large-scale image data set which can be used freely,the shading model with strong generalization ability is trained,so as to realize the automation of grayscale image coloring.Aiming at the problems of low color saturation,insufficient color content and even partial color mismatching in the colorization algorithms based on deep learning,this paper deeply analyzes the causes of these problems.In essence,the coloring algorithm proposed so far only optimizes the Euclidean distance between the predicted result and the real picture,but this method that takes the Euclidean distance as the loss function does not have the robustness for the inherent ambiguity and multimodality of the coloring problem.If an object can have several different colors,the optimal solution to the loss function will be the average of those colors,resulting in visually gray-looking,unsaturated results.Therefore,this paper improves the above algorithm and regards the coloring of gray image as a multi-classification problem rather than a traditional regression problem.Taking the cross entropy of multiple classification as the loss function,this paper proposes a coloring algorithm of gray image based on convolutional neural network.At the same time,aiming at the specific problem of face gray image coloring,this paper proposes a color algorithm of face gray image based on conditional DCGAN.The effectiveness of the two algorithms is proved by comparing the results of image coloring under different algorithms.The main works of this paper are as follows:(1)The theoretical knowledge related to the grayscale image color algorithm is introduced in detail.This paper first introduces the common color space in gray image colorization algorithm and points out the advantages and disadvantages of each color space and its application range.The basic knowledge and optimization method of convolutional neural network are introduced emphatically.At the same time,the residual network and the generative adversarial networks are also introduced.(2)Deeply analyzed the classical traditional grayscale image color ing algorithm and its defects.The traditional coloring method not only needs human interaction and consumes time,but also is difficult to process.And with the increase of the size of the image to be colored,the coloring speed decreased significantly.The problem of color averaging of coloring effect in the proposed deep learning based coloring algorithm is analyzed,and it is concluded that the reason of this problem is the improper selection of loss function.(3)A grayscale image color algorithm based on convolutional neural network is proposed.Different from the existing methods that regard color prediction as a regression problem,this paper regards it as a multi-classification problem.The multimodal properties of color prediction are modeled by quantifying ab color channel in Lab color space.Since the value of ab in the natural image tends to be smaller,this paper use the classification rebalancing technique to balance the color categories that appear less frequently in the training,so as to maintain the color diversity.(4)A coloration algorithm of gray image of face based on DCGAN is proposed.DCGAN fuses the convolutional neural network and the generative adversarial networks together.By utilizing the special training mechanism of the adversarial networks,the generator tries to generate more authentic color images to deceive the discriminator,which will judge the authenticity of the input images and feed back to the generator.The generator and discriminator promote and play with each other to complete the coloring of gray image.In this paper,the experiment proves that generative adversarial networks can produce rich coloring effect.