Behind the Tech
Automatic image background removal is a challenging problem in the field of computer vision and image processing. It's a problem that doesn't necessarily have a perfect solution, but rather a series of "good-enough" solutions, depending on the specific requirements and contexts. There are traditional methods to tackle this issue, but advancements in deep learning have introduced more sophisticated solutions. Our service primarily relies on a deep learning, utilizing a complex and nuanced architecture to achieve high-quality results.
The quality and effectiveness of any deep learning model are heavily dependent on the dataset used for training. Recognizing this, we have dedicated significant effort and investment to develop and refine our dataset. It forms the backbone of our background removal service, ensuring that our deep learning models are trained on high-quality, diverse data sources. For a deeper understanding of the diversity and challenges our dataset presents, please explore our publicly available dataset of 100 image and alpha matte pairs: withoutbg100 image matting dataset.
Also, you can read how we can generate the training data.
In our approach, we handle different groups of photos with tailored processing techniques. For instance, images with fur or hair require different treatment compared to those with transparent objects. We also implement background randomization in our dataset creation. This technique is cost-effective, but it poses its own challenges, such as not accurately representing real-world scenarios. To mitigate this, we perform color matching either manually or using image harmonization techniques. Additionally, we use Generative Adversarial Networks (GANs) to assess whether an image appears synthetic or not.
Our solution is grounded in Convolutional Neural Networks (CNNs). We employ a set of encoder-decoders that operate in parallel, each contributing to the final outcome. The results from these individual models are then fused by a final model, which ensures that the output is not just accurate but also seamless.
In pursuit of achieving sharper and more precise results, we utilize a combination of alpha prediction loss and compositional loss. This dual approach allows our model to finely tune the boundaries and details in the images, ensuring that the background removal is not just effective, but also visually pleasing and realistic.