GAN Transformer Colorization for Realistic Black-and-White Images
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Solution Overview
Problem
Existing methods for colorizing black and white images using Generative Adversarial Networks (GANs) often fail to produce high-quality color images that accurately resemble real images, particularly when utilizing transformer blocks.
Innovation Solution
A GAN-based model incorporating a transformer block with specific architectural components, including Depth Wise Convolution (DWC) layers, Window-based Multi-head Self Attention (W-MSA) layers, and Colorization Feed Forward (CFF) blocks, is used to generate color images from black and white images, utilizing a generator network with an encoder-decoder architecture and trained with a total loss function combining pixel-wise, VGG, and WGAN loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a standard GAN-based model is used for colorization, then the model structure is simple and easy to implement, but the colorization quality is insufficient and does not closely resemble real images
Solution Approach 1:
The model is divided into distinct functional modules: encoder, transformer blocks with W-MSA layers, decoder, and discriminator. Each module has a specific function in the colorization pipeline, allowing for targeted optimization of colorization quality while maintaining manageable complexity through modular design.
Solution Approach 2:
The introduction of transformer blocks with multi-head self-attention mechanisms adds a new dimensional approach to feature processing beyond traditional convolutional operations. This enables the model to capture global dependencies and contextual relationships in the image data, significantly improving colorization quality.
2Manufacturing precision
If transformer blocks are added to improve colorization quality, then the color conversion capability is enhanced, but the computational complexity and processing time increase
Solution Approach 1:
The feature processing is segmented into multiple parallel attention heads within the W-MSA layer, each handling different aspects of feature relationships. This parallelization allows the model to process complex transformations simultaneously, improving accuracy while managing computational time through efficient parallel computation.
Solution Approach 2:
The model applies multiple transformer blocks with W-MSA layers in sequence, performing more attention operations than a standard model. This excessive action in the transformation process ensures thorough feature processing and high colorization accuracy, with the computational cost justified by the significant quality improvement.
3Manufacturing precision
If W-MSA layers with shifted windows are used, then the feature map processing capability is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The feature map is divided into multiple non-overlapping windows, and the W-MSA layer processes each window independently with its own attention mechanism. This segmentation allows the model to capture local features efficiently while the shifted window strategy across layers ensures global context is captured, improving feature processing quality with manageable computational complexity.
Data Source
AI summary
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.