“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping.

/pdf/a-gan-based-approach-toward-architectural-line-drawing-23zx7vr7vo.pdf

A GAN-based approach toward architectural line drawing colorization prototyping

Data hiding method embed secret data inside a cover medium. Image quality and capacity plays a significant role in the performance of data hiding. The motivation of this paper is to improve the interpolation technique and bit plane data hiding method using edge quad-tree block complexity. Edges in the cover image are identified using edge detector and are further partitioned using quad-tree. The edges are smooth regions divided into smaller blocks in order to obtain good embedding capacity, whereas the rough regions involve those regions other than the edges and are kept as larger blocks to avoid distortion. Each quad-tree block is implemented with up-sampling interpolation based on edges and every pixel is divided into two-bit plane namely high and low bit plane. In each bit plane, two different data are embedded based on their hiding capacity. The hiding capacity of the high bit plane is calculated by two prediction levels namely Pixel Value Differencing and block complexity. In low bit plane, Least Significant Bit method is used for hiding the data. Experimental results demonstrate that the proposed method significantly improved the embedding performance, capacity and also resist to attacks when compared to other state-of-the-art methods.

A novel data hiding by image interpolation using edge quad-tree block complexity

Damage analysis and quantification of RC beams assisted by Damage-T Generative Adversarial Network

The Art of Architectural Drawing: Imagination and Technique

The invention discloses a CycleGAN-based image training network structure ArcGAN and a method, the network structure ArcGAN is composed of a generator and double discriminators, and the double discriminators comprise a rough discriminator and a fine discriminator, wherein the encoder comprises an input layer and three down-sampling convolution layers, and each down-sampling convolution layer is connected with two flat convolution layers with the same structure as the input layer; a converter comprises five dense convolution blocks without pooling layers, each dense convolution block comprisesfive dense convolution layers with bottleneck layers, and a compression layer is arranged between the blocks, wherein the decoder comprises three upsampling deconvolution layers and an output layer, and each upsampling deconvolution layer is connected with two flat convolution layers with the same structure as the input layer; and each layer of downsampling in the encoder and the upsampling in thedecoder corresponding to the layer of downsampling are in replicated connection. A rough discriminator is used for processing high-level visual information and consists of six down-sampling layers and an output layer; the calculation loss of the fine discriminator is combined with the calculation loss of the rough discriminator, and the fine discriminator and the rough discriminator jointly complete confrontation consistency training.

CycleGAN-based image training network structure ArcGAN and method

Architectural illustration using line drawing with colorization is an important tool and art format. In this paper, in order to generate a natural-looking and high quality watercolorlike colorization for architectural line drawing, we propose a novel Generative Adversarial Network (GAN) approach, namely ArchGANs. The proposed ArchGANs unifies a line-feature-aware stylized colorization network (ArchColGAN), which can learn, predict and generate the coloring based on a dataset, as well as a shading generation network (ArchShdGAN), which augments the illustration with controllable lighting effects for better depicting building in 3D. Specifically, ArchColGAN can preserve the essential line features and building part correlation property, it also tackles the uneven colorization problem caused by the sparse lines. Experimental results demonstrate our proposed method is effective and suitable for colorization prototyping.

ArchGANs: stylized colorization prototyping for architectural line drawing

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A GAN-based approach toward architectural line drawing colorization prototyping

CycleGAN-based image training network structure ArcGAN and method

ArchGANs: stylized colorization prototyping for architectural line drawing