Authors :
Sumit Gunjate; Tushar Nakhate; Tushar Kshirsagar; Yash Sapat
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Y1l7U6
DOI :
https://doi.org/10.5281/zenodo.7588232
Abstract :
With the development of the modern age and
its technologies, people are discovering ways to improve,
streamline, and de-stress their lives. A difficult issue in
computer vision and graphics is the creation of realistic
visuals from hand-drawn sketches. There are numerous
uses for the technique of creating facial sketches from
real images and its inverse. Due to the differences
between a photo and a sketch, photo/sketch synthesis is
still a difficult problem to solve. Existing methods either
require precise edge maps or rely on retrieving
previously taken pictures. In order to get around the
shortcomings of current systems, the system proposed in
this paper uses generative adversarial networks. A type
of machine learning method is called a generative
adversarial network (GAN). This algorithm pits two or
more neural networks against one another inthe context
of a zero-sum game. Here, we provide a generative
adversarial network (GAN) method for creating
convincing images. Recent GAN-based techniques for
sketch-to-image translation issues have produced
promising results. Our technology produces photos that
are more lifelike than those made by other techniques.
According to experimental findings, our technology can
produce photographs that are both aesthetically pleasing
and identity-Preserving using a variety of difficult data
sets.
Keywords :
Image Processing, Photo/Sketch Synthesis.
With the development of the modern age and
its technologies, people are discovering ways to improve,
streamline, and de-stress their lives. A difficult issue in
computer vision and graphics is the creation of realistic
visuals from hand-drawn sketches. There are numerous
uses for the technique of creating facial sketches from
real images and its inverse. Due to the differences
between a photo and a sketch, photo/sketch synthesis is
still a difficult problem to solve. Existing methods either
require precise edge maps or rely on retrieving
previously taken pictures. In order to get around the
shortcomings of current systems, the system proposed in
this paper uses generative adversarial networks. A type
of machine learning method is called a generative
adversarial network (GAN). This algorithm pits two or
more neural networks against one another inthe context
of a zero-sum game. Here, we provide a generative
adversarial network (GAN) method for creating
convincing images. Recent GAN-based techniques for
sketch-to-image translation issues have produced
promising results. Our technology produces photos that
are more lifelike than those made by other techniques.
According to experimental findings, our technology can
produce photographs that are both aesthetically pleasing
and identity-Preserving using a variety of difficult data
sets.
Keywords :
Image Processing, Photo/Sketch Synthesis.