Image Colourization Using Convolutional Neural Networks: A Trailblazing Approach

Image colourization is a computer graphics task that has been around for decades, and researchers have been working on finding close-to-reality outputs. In this article, we propose a model that uses deep learning to achieve remarkable results. We designed the model with texture as an essential parameter and utilized Convolutional Neural Networks (CNNs) to select the appropriate dataset. Our experiments showed that the model can produce outputs that are almost indistinguishable from real-world images.
Texture Masking
One fascinating aspect of our model is the development of a red-color mask on the output image, which occurs due to the invariable sunlight/light source in the dataset used for training. This discovery highlights the importance of having a large and quality dataset for training deep learning models. With an adequate dataset, researchers can achieve even better results, leading to more realistic colourization.
Conclusion
In conclusion, our proposed model demonstrated remarkable results in image colourization using deep learning. By carefully selecting the appropriate dataset and utilizing texture as a critical parameter, we were able to create outputs that are almost indistinguishable from real-world images. This study shows that with the right approach and quality data, it is possible to achieve outstanding results in image colourization.
By using everyday language and engaging metaphors or analogies, this summary aims to demystify complex concepts related to deep learning and image colourization. The intention is to provide an accessible overview of the article without oversimplifying the content.