Introduction
Imagine you have a photo that you love, but it has a scratch, a stain, or an unwanted object in it. Or maybe you want to use an incomplete image as a starting point for learning something new. Wouldn’t it be great if you could fill in the missing parts of the image and make it look realistic and coherent? This is what image inpainting is all about: synthesizing the missing regions in an image.
But image inpainting is not an easy task. It requires a lot of computing power and data to train a model that can generate realistic and coherent results. Most of the existing models for image inpainting are based on complex architectures that use transformers or convolutional neural networks (CNNs) and are trained in adversarial or diffusion settings. These models are very powerful, but they also have some drawbacks: they are slow, expensive, and hard to generalize.
That's why A new model that is a resource-efficient token-mixer for self-supervised inpainting developed by students/researchers from the Department of Electrical Engineering at the Indian Institute of Technology Bombay. The model was developed to address the issue of computationally heavy state-of-the-art models for image inpainting that are based on transformer or CNN backbones trained in adversarial or diffusion settings. This new model is called 'WavePaint'.
What is WavePaint?
WavePaint is a cutting-edge model that uses a computationally efficient WaveMix-based fully convolutional architecture. Unlike traditional vision transformers, WavePaint employs a 2D-discrete wavelet transform (DWT) for spatial and multi-resolution token-mixing, along with convolutional layers. This unique approach allows WavePaint to efficiently process and synthesize image data.
Key Features of WavePaint
- Resource efficiency: WavePaint outperforms current state-of-the-art models for image inpainting on reconstruction quality while using less than half the parameter count and considerably lower training and evaluation times.
- Unique architecture: WavePaint uses a computationally efficient WaveMix-based fully convolutional architecture that diverges from vision transformers.
- Advanced technology: WavePaint employs a 2D-discrete wavelet transform (DWT) for spatial and multi-resolution token-mixing, along with convolutional layers.
- Efficient generalization: Neural architectures modeled after natural image priors, such as WavePaint, require fewer parameters and computations to achieve generalization comparable to transformers.
Capabilities/Use Case of WavePaint
Some potential use cases for WavePaint include:
- Restoring occluded or degraded areas in images: You can use WavePaint to fix scratches, stains, or unwanted objects in your photos or paintings. You can also use it to restore old or damaged images, such as historical photos or artworks.
- Serving as a precursor task for self-supervision: You can use WavePaint to mask out parts of an image and inpaint them, and then use the inpainted image as a source of supervision for other tasks, such as classification, segmentation, or detection. This way, you can leverage the unlabeled data and learn useful features without human intervention.
- Outperforming current GAN-based architectures in image inpainting tasks: You can use WavePaint to generate realistic and coherent inpainted results that surpass the current GAN-based architectures in terms of quality and diversity. You can also avoid the challenges of training GANs, such as mode collapse, instability, and high computational cost.
How does WavePaint work?
WavePaint uses a computationally efficient WaveMix-based fully convolutional architecture to perform image inpainting. The model diverges from traditional vision transformers by using a 2D-discrete wavelet transform (DWT) for spatial and multi-resolution token-mixing, along with convolutional layers. This approach allows WavePaint to achieve generalization comparable to transformers while requiring fewer parameters and computations.
source - https://arxiv.org/pdf/2307.00407v1.pdf
The input image is first masked with a binary mask generated by a mask generator. The masked image is then passed through a series of Wave modules, which process the input and produce the output. Each Wave module consists of four WaveMix blocks, which perform token-mixing using DWT and inverse DWT (IDWT), along with a depth-wise convolution layer for further spatial token-mixing. The output of the last WaveMix block is then passed through a Decoder module, which applies IDWT to reconstruct the image from the four sub-bands. The reconstructed image is then passed through a final convolution layer to generate the final inpainted image.
The WaveMix block is the fundamental building block of the WaveMix architecture, allowing multi-resolution token-mixing using 2D-DWT. This helps in rapid expansion of the receptive field and reduces computational burden. The DepthConv layer employs a depth-wise convolution operation followed by a GELU non-linearity and batch-normalization. The Decoder module is used to up-sample the resolution of feature maps back to the original input resolution using a transposed convolution layer followed by batch-normalization.
Overall, the unique architecture and design of WavePaint allow it to efficiently process and synthesize image data at different scales and frequencies, making it a powerful tool for image inpainting.
Performance Evaluation
WavePaint has been evaluated against current state-of-the-art models for image inpainting and has been found to outperform them on reconstruction quality while also using less than half the parameter count and considerably lower training and evaluation times. It even outperforms current GAN-based architectures in the CelebA-HQ dataset without using an adversarially trainable discriminator.
The CelebA-HQ dataset is a high-quality version of the CelebA dataset that consists of 30,000 images at 1024×1024 resolution. The research paper reports the quantitative evaluation metrics of inpainting by WavePaint of different sizes by varying the number of modules and WaveMix blocks per modules. All models use level-1 2D DWT. The models were evaluated on 2000 images of CelebA-HQ that LaMa used for testing . The quantitative performance of WavePaint using different hyperparameters on the CelebA-HQ dataset is as shown in above image.
According to the information available on the "Papers with Code" website, WavePaint ranks #1 on Image Inpainting on ImageNet and #2 on Image Inpainting on CelebA-HQ. The website links are available at the end of this article.
How to access and use this model?
WavePaint is available on GitHub, where you can find instructions on how to use the model locally. The model is open-source under the MIT license, which means that it is free to use, distribute, and modify.
The MIT license is a permissive free software license that allows users to do virtually anything they want with the software, including using it for commercial purposes. This means that you can use WavePaint for your own projects, whether they are personal or commercial in nature.
if you’re interested in using WavePaint, you can access the model on GitHub and follow the instructions provided to use it locally. The model is open-source under the MIT license, which gives you a lot of freedom in how you use it. All relevant links related to the model are provided under the 'source' section at the end of this article.
Conclusion
WavePaint represents a significant advancement in the field of image inpainting. The impact of WavePaint on the future journey of artificial intelligence is potentially significant. By demonstrating that neural architectures modeled after natural image priors can achieve generalization comparable to transformers with fewer resources, WavePaint opens up new avenues for research and development in the field of image inpainting and beyond. Its success could inspire further innovation and progress in the development of resource-efficient models for a wide range of applications.
Source
research paper - https://arxiv.org/abs/2307.00407v1
research document - https://arxiv.org/pdf/2307.00407v1.pdf
Git hub repo - https://github.com/pranavphoenix/WavePaint
License - https://github.com/pranavphoenix/WavePaint/blob/main/LICENSE
research paper on paperwithcode - https://paperswithcode.com/paper/wavepaint-resource-efficient-token-mixer-for
Ranked #1 on Image Inpainting on ImageNet - https://paperswithcode.com/sota/image-inpainting-on-imagenet
Ranked #2 on Image Inpainting on celeba-hq - https://paperswithcode.com/sota/image-inpainting-on-celeba-hq
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