Self-Supervised Image Classification From Table 3, we can see that the PSNR and SSIM values are higher after adding the classification subnetwork and AFF module, which indicates that the classification subnetwork and AFF module can significantly improve the colorization effect of the colorization network. Accessibility Our classification loss with rebalancing produces more accurate and vibrant results than a regression loss or a classification loss without rebalancing. Larsson et al. Naturalness of user study. : Optimization by simmulated annealing. We propose the use of deep learning, specifically the use of convolution neural networks. Second, we construct a classification subnetwork to constrain the colorization network with category loss, which improves the colorization accuracy and saturation. The color transfer method reduces some manual operations, but the colorization effect depends on the reference picture and the selection of the color transfer method. Each pair consisted of a color photo next to a re-colorized version, produced by either our algorithm or a baseline. The SSIM takes values from 0 to 1, and a larger SSIM value means that the two images are more similar. However, most colorization algorithms use regression loss functions (such as L1 and L2) [9,10,11,12,13,14,15,16,17,18,19,20,21]. Colorization effect of pixel-level network. In order to verify the effectiveness of our colorization algorithm, in this paper, we compare our colorization algorithm with those of Larsson et al. Graph. Please visit http://richzhang.github.io/colorization/ to see the full range of results and to try our model and code. , Zhang et al. Each of these pairs was scored by at least 10 participants. Furthermore, we propose a U-Net-based colorization network. Across all three classification tests, we achieve state-of-the-art accuracy. We account for the class-imbalance problem by reweighting the loss of each pixel at train time based on the pixel color rarity. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. We train our network on the 1.3M images from the ImageNet training set[28], validate on the first 10k images in the ImageNet validation set, and test on a separate 10k images in the validation set, same as in[23]. Without accounting for this, the loss function is dominated by desaturated ab values. Science 220(4598), 671680 (1983), CrossRef Others have noted the easy availability of training data, and previous works have trained convolutional neural networks (CNNs) to predict color on large datasets [1, 2]. Evaluating the quality of synthesized images is well-known to be a difficult task, as simple quantitative metrics, like RMS error on pixel values, often fail to capture visual realism. Random initialization, with Gaussian weights or the k-means scheme implemented in [36], peak in the middle layers. Before input MinRn*c*h*w passes through the convolutional block, it is concatenated with the same size features of the AFF module output. 2022 Springer Nature Switzerland AG. Figure3(b) shows the empirical distribution of pixels in ab space, gathered from 1.3M training images in ImageNet [28]. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 15 March 2020; New York, NY, USA: IEEE; 2020. pp. Moreover, we show that colorization can be a powerful Quantitative experiments show that our colorization network has higher PSNR and SSIM values of 25.8803 and 0.9368. Towards Vivid and Diverse Image Colorization with Generative Color Prior; Proceedings of the IEEE/CVF International Conference on Computer Vision; Montreal, QC, Canada. Example input grayscale photos and output colorizations from our algorithm. In: Kotz, S., Johnson, N.L. Before 7). (eds.) Previous [10, 14] and concurrent [16] self-supervision methods are shown. Upsk denotes the upsampling amplification k times. 649666Cite as, 642 This is probably caused by two reasons. Asymmetric feature fusion module structure. Springer, Heidelberg (1992), Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. Therefore, PAN image colorization is a worthy research topic. We then sweep across thresholds from 0 to 150 to produce a cumulative mass function, as introduced in [22], integrate the area under the curve (AuC), and normalize. AFF1,AFF2,AFF3 are formulated as follows: where AFFnout denotes the output of the nth layer, Enn denotes the output of the nth convolutional block of the encoder, Subsk denotes downsampling by a factor of k, and Upsk denotes upsampling by a factor of k. We construct the inverse color category matrix M1 indexing the values of a and b through x0=M1q, where q is the color category of pixel and M1 is the inverse of the color category matrix M. The index of M1 is the color category q, corresponding to a0,b0 of q. We perform this task in CIE Lab color space. For a given input \(\mathbf {X}\), we learn a mapping \(\mathbf {\widehat{Z}} = \mathcal {G}(\mathbf {X})\) to a probability distribution over possible colors \(\mathbf {\widehat{Z}}\in [0,1]^{HxWxQ}\), where Q is the number of quantized ab values. In: Proceedings of the IEEE International Conference on Computer Vision, pp. [12] constructed conditional generative adversarial networks (cGANs) to build colorization networks. Example results from our ImageNet test set. images. Results are shown in Table2. The generated colorful images have been relatively satisfactory, but the problem of brown and unsaturated generated images has persisted, as shown in Figure 1. Based on this, Irony et al. (eds.) Figure6 gives a better sense of the participants competency at detecting subtle errors made by our algorithm. Citations, Part of the Lecture Notes in Computer Science book series (LNIP,volume 9907). Kindly check and confirm if the inserted page range is correct for Ref. Springer, Heidelberg (2008), CrossRef The As shown in Figure 6, our algorithm generates more vivid and saturated color images as compared with Larsson et al., Iizuka et al., Deoldify, and Su et al. As shown in Figure 2, our colorization network consists of three parts: an encoder, a classification subnetwork, and a decoder. 10971105 (2012), Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. These results validate the effectiveness of using both a classification loss and class-rebalancing. Close inspection reveals that on these images, our colorizations tend to have giveaway artifacts, such as the yellow blotches on the two trucks, which ruin otherwise decent results. Colorful Image Colorization. : Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. Manga Colorization. Using the existing datasets and our new one, we perform an extensive experimental evaluation of existing image colorization methods. According to the loss function of colorization, they are divided into two types: regression loss function and classification loss function. calculated the geometric distance between each pixel point a and b value and its 32 closest color categories a and b values. A collection of Deep Learning based Image Colorization and Video Colorization papers. Yatziv L., Sapiro G. Fast image and video colorization using chrominance blending. Su et al. Our network, trained on classification without rebalancing, outperforms our L2 variant (when trained from scratch). 5. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Pattern Anal. Given an input lightness channel \(\mathbf {X}\in \mathbb {R}^{H\times W\times 1}\), our objective is to learn a mapping \(\mathbf {\widehat{Y}} = \mathcal {F}(\mathbf {X})\) to the two associated color channels \(\mathbf {Y} \in \mathbb {R}^{H\times W \times 2}\), where H,W are image dimensions. Deep Learning Applications (Darknet - YOLOv3, YOLOv4 | DeOldify - Image Colorization, Video Colorization | Face-Recognition) with Google Colaboratory - on the free Tesla K80/Tesla T4/Tesla P100 GPU - using Keras, Tensorflow and PyTorch. We then use multinomial cross entropy loss \(\text {L}_{cl}(\cdot ,\cdot )\), defined as: where \(v(\cdot )\) is a weighting term that can be used to rebalance the loss based on color-class rarity, as defined in Sect. Finally, to make the colorization vivid, this work balanced the weights using the color category probability distribution of the ImageNet training set. the contributions of this paper include: (1) histogram equalization effectively improves the visual effect and the colorfulness of overexposed and underexposed images; (2) the introduction of semantic segmentation network accelerates the edge convergence of the image and improves the positioning accuracy of the algorithm, and solves the problem A picture can take up to one month to colorize. ndim should match the dimensionality of the image the kernel will be applied to. Deep neural networks proved a great success in different fields recently. Ranked #102 on The decoder consists of three layers of convolutional blocks. The learning rate decays gradually with training, and col and cls are set to 1 and 0.003, respectively. Luan Q., Wen F., Cohen-Or D., Liang L., Xu Y.-Q., Shum H.-Y. To fairly compare to previous feature learning algorithms, we retrain an AlexNet[38] network on the colorization task, using our full method, for 450k iterations. Next, we use M1 to index the a,b value x0R2*h/4*w/4. In all pairs to the left of the dotted line, participants believed our colorizations to be more real than the ground truth on \(\ge 50\,\%\) of the trials. ACM (2001), Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. (Pull Request is preferred) Outline. [11], Zhang et al. One can see that our model is still able to produce good colorizations, even though the low-level image statistics of the legacy photographs are quite different from those of the modern-day photos on which it was trained. The category balance module obtains the corresponding balance weight (Zh,w) based on the color category Zh,w. 14221430 (2015), Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. Charpiat, G., Hofmann, M., Schlkopf, B.: Automatic image colorization via multimodal predictions. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (. We use 50,000 images from the ImageNet validation set for testing and adjust the resolution of the generated images to 256 256. Features are average-pooled, with equal kernel and stride sizes, until feature dimensionality is below 10k. In the following, we focus on the design of the objective function, and our technique for inferring point estimates of color from the predicted color distribution. Abstract The article discusses one of the latest ways to colorize a black and white image using deep learning methods. [24], Hariharan, B., Arbelez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. The SSIM can sensitively perceive the local structural differences between the two pictures. For a comparison on a full selection of random images, please see our project webpage. (TOG) 27, 152 (2008). Manga Colorization. Cheng Z., Yang Q., Sheng B. PDF | As we know, image colorization is widely used in computer graphics and has become a research hotspot in the field of image processing. and M.L. (Color figure online). Using regression loss functions (such as Iizuka et al. 27942802 (2015), Donahue, J., Krhenbhl, P., Darrell, T.: Adversarial feature learning. We test how well our model performs in generalization tasks, compared to previous [8, 10, 14, 15] and concurrent [16] self-supervision algorithms, and find that our method performs surprisingly well, achieving state-of-the-art performance on several metrics. We tested our algorithm on 50,000 images from the ImageNet validation set against the algorithms of Larsson et al. (Color figure online). After adding the classification subnetwork, the color of hand and mushroom are more vivid, but the tip of thumb still have green color overflow. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. Quantitative analysis of colorization effect. This problem is Note that this metric is dominated by desaturated pixels, due to the distribution of ab values in natural images (Fig. use a two-stream architecture in which they fuse global and local features, and we use a single-stream, VGG-styled network with added depth and dilated convolutions [26, 27]. A total of 40 participants evaluated each algorithm. Conceptualization, Z.W., Y.Y., D.L., Y.W. Therefore, our task becomes much more achievable: to model enough of the statistical dependencies between the semantics and the textures of grayscale images and their color versions in order to produce visually compelling results. SIGGRAPH 2016) 35(4), 110 (2016). Graph. use a regression loss. To encourage diversity in colorization, we construct the balance weight matrix , which is formulated as follows: where Q represents the number of color categories used, here is 313; represents the weight of mixing the average distribution of each color category and the color category distribution of the ImageNet training set of 1.28 million images, and 0.5 was set. The color category probability distribution becomes xab after the color recovery (CRC) module xab concentrates xl to obtain the colorful image xlab. In this paper, we propose a new method to compute color categories and balance the weights of color images. If an object can take on a set of distinct ab values, the optimal solution to the Euclidean loss will be the mean of the set. In: International Conference on Learning Representations (2016), Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. In recent years, with the rapid development of deep learning, a large number of automatic colorization algorithms based on convolutional neural networks (CNNs) have been proposed. Received 2022 Aug 23; Accepted 2022 Oct 18. ; resources, Z.W., Y.Y. Our method not only provides a useful graphics output, but can also be viewed as a pretext task for representation learning. Available online: Su J.-W., Chu H.-K., Huang J.-B. Please visit http://richzhang.github.io/colorization/ to see the full range of results. It can be clearly seen that our colorization network has higher SSIM and PSNR values, which means the colorization effect of our network is better. To begin, our conv1 representation results in worse linear classification performance than competiting methods[14, 16], but is comparable to other methods which have a grayscale input. In [15], a convolutional neural network (CNN) which accepts black and white images as input is designed and constructed, and a statistical learning driven method is used to solve the problem of. The color categories and balance weights were formulated as follows: where d is the geometric distance between pixel point a and b values and its 32 closest color categories a and b values; h and w are the positions of the pixel; q is the color category of the pixel; is the Gaussian kernel with Gaussian weighting, which is taken as 0.5 here; p is the color category distribution of all pixels in the ImageNet training set images; Q represents the number of color categories used, which is 313 in this study; represents the weight of mixing the average distribution of each color category and the color category distribution of the ImageNet training set of 1.28 million images, and 0.5 was tested to be the most effective. First, our classification of color categories is not accurate enough. 569593. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural. Observe that the number of pixels in natural images at desaturated values are orders of magnitude higher than for saturated values. The results are shown in the second column from the right of Table1. In: European Conference on Computer Vision (2016), Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color!
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