View source for Industry-Academic Cooperation Foundation, Chosun University (20240265587). ELECTRONIC DEVICE FOR COLORIZING BLACK AND WHITE IMAGE USING GAN BASED MODEL COMPRISING TRANSFORMER BLOCK AND METHOD FOR OPERATION THEREOF simplified abstract

=ELECTRONIC DEVICE FOR COLORIZING BLACK AND WHITE IMAGE USING GAN BASED MODEL COMPRISING TRANSFORMER BLOCK AND METHOD FOR OPERATION THEREOF=


==Organization Name==

[[:Category:Industry-Academic Cooperation Foundation, Chosun University|Industry-Academic Cooperation Foundation, Chosun University]]


[[Category:Industry-Academic Cooperation Foundation, Chosun University]]

==Inventor(s)==

[[:Category:Bum Shik Lee of Gwangju (KR)|Bum Shik Lee of Gwangju (KR)]][[Category:Bum Shik Lee of Gwangju (KR)]]

[[:Category:Muhammad Hamza Shafiq of Gwangju (KR)|Muhammad Hamza Shafiq of Gwangju (KR)]][[Category:Muhammad Hamza Shafiq of Gwangju (KR)]]

==ELECTRONIC DEVICE FOR COLORIZING BLACK AND WHITE IMAGE USING GAN BASED MODEL COMPRISING TRANSFORMER BLOCK AND METHOD FOR OPERATION THEREOF - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240265587 titled 'ELECTRONIC DEVICE FOR COLORIZING BLACK AND WHITE IMAGE USING GAN BASED MODEL COMPRISING TRANSFORMER BLOCK AND METHOD FOR OPERATION THEREOF

'''Simplified Explanation:'''
This patent application describes an electronic device that colorizes black and white images using a generative adversarial network (GAN) model with transformer blocks.

* The device processes black and white images to generate colorized versions by applying them to the GAN-based model.
* The GAN model includes a generator network with transformer blocks, convolution layers, and transpose convolution layers.
* Each transformer block in the generator network consists of various layers such as depth-wise convolution, layer normalization, multi-head self-attention, and colorization feed-forward block.

'''Key Features and Innovation:'''
* Utilizes a GAN-based model with transformer blocks for colorizing black and white images.
* Incorporates various layers in the generator network to enhance color conversion.
* Enables the generation of pseudo color images based on the luminance and chrominance channels of the input black and white images.

'''Potential Applications:'''
* Image editing and enhancement software.
* Historical photo colorization tools.
* Artistic rendering applications.

'''Problems Solved:'''
* Efficient colorization of black and white images.
* Enhancing visual appeal and realism of monochromatic images.
* Streamlining the process of adding color to grayscale photos.

'''Benefits:'''
* Automated colorization process.
* Improved visual quality of black and white images.
* Versatile applications in various industries such as design, entertainment, and historical preservation.

'''Commercial Applications:'''
Colorization technology for photo editing software: Enhances user experience and expands the capabilities of image editing tools in the market.

'''Questions about Colorization Technology:'''
1. How does the transformer block in the GAN model contribute to the colorization process?
2. What are the potential challenges in accurately colorizing black and white images using this technology?

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==Original Abstract Submitted==

in accordance with various embodiments, an electronic device for colorizing a black and white image using a generative adversarial network (gan)-based model comprising a transformer block includes a processor, wherein the processor is set to: obtain a black and white image including only first information about a luminance channel; and generate a pseudo color image including only second information about a chrominance channel by applying the black and white image to the gan-based model, the gan-based model includes a generator network including a plurality of transformer blocks for color conversion, a plurality of convolution layers, and a plurality of transpose convolution layers, the plurality of transformer blocks each include a depth wise convolution (dwc) layer, a first layer normalization (ln) layer, a window-based multi-head self attention (w-msa) layer, a second ln layer, and a colorization feed forward (cff) block. other various embodiments are possible.



[[Category:G06T11/00]]
[[Category:CPC_G06T11/001]]