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AFFILIATIONS: Al ROBOLAB. Luxembourg University
AUTHORS: MURUGARAJ Keerthana, Ingo Schandeler
Many people are wondering about the emergence of Al-art, a new genre of art. With the advancement of Al, the artists would be able to express themselves without having to go through the process of creating a work of art. Al-art is more appropriately referred to as neural network art, is created utilizing sophisticated algorithms. The role of Al in art creation and the artist-Al interaction are actively evolving.
Al is assisting artists in several ways, including assisting them reproduce the styles of famous painters from the past. This capacity to imitate and recreate can be beneficial in both the creation and restoration of a work.
We try to do such experimental project on topic ‘Automatic Mixed Painting’, (a creative Art-Deep Painting Style transfer and Harmonization), which is basically a Image Composition and Harmonization technique using Deep Convolutional Neural networks (CNN). This project combines both content addition and style transfer. Copying an element from a painting/image and pasting it into another painting/image is a challenging task Our aim is to impose a painting (or a portion of that one) into another painting so that it looks like genuine painting. We are working on an algorithm that will significantly improve the quality of creative painting outputs.
The simplest way to blend images is to combine the foreground and background colour values using linear interpolation, which is often accomplished using alpha matting Gradient-domain compositing (or Poisson blending) was first introduced by Perez et al. which considers the boundary condition for seamless cloning. Deep neural networks further improved colour properties of the composite by learning to improve the overall photo realism. Multi-Scale Image Harmonization introduced smooth histogram and noise matching which handles fine texture on top of colour, however it does not capture more structured textures like brush strokes which often appear in paintings. Image Melding combines Poisson blending with patch-based synthesis in a unified optimization framework to harmonize colour and patch similarity. Camouflage Images proposed an algorithm to embed objects into certain locations in cluttered photographs with a goal to make the objects hard to notice. While these techniques are mostly designed with photographs in mind, our focus is on paintings. We are interested in the case where the background of the composite is a painting.
Style Transfer using Neural Networks. Recent work on Neural Style transfer has shown impressive results on Neural Style transfer has shown impressive results on transferring the style of an artwork by matching the statistics of layer responses of a deep neural network. These methods transfer arbitrary styles from one image to another by matching the correlations between feature activations extracted by a pretrained deep neural network on image classification. The reconstruction process is based on an iterative optimization framework that minimizes the content and style losses computed from the VGG neural network. With regards to photographic exchange, Luan et al. limit confounds utilizing scene analysis. Gatys et al. fasten up the style transferring process by producing a large size, high quality stylisation using Guided Gram Matrixes and colour histogram matchings. Ongoing methodologies supplant the Gram lattice with coordinating different measurements of neural responses. Liao et al. further improve the nature of the outcomes by presenting bidirectional dense correspondence field matches.
The main idea is to transfer relevant characteristics of the painting on the embedded object and further to improvise the output quality. For this approach two-pass mechanism can be used: First pass (Robust Coarse Harmonization) and the Second Pass (High-Quality Refinement). The first pass is to achieve the harmonization task by performing a rough match of the colour and texture properties of the pasted region to those of semantically similar regions in the painting. The second pass is to enhance the quality of resultant obtained from the first pass.Basically, we have a style image (target painting), and the content image consists of a component of a painting to be added to the style image. There is also a plan to use the mask image (optional) of the added object. The mask image is used to compute the loss functions only for that part of the image. But the Content Image is manually done or should try with image composition technique. we need to cut and paste the object into our target painting and send them along with mask image for Image harmonization.