Wavelet-integrated Identity Preserving Adversarial Network for face super-resolution

WIPA: A Technique for Super-Resolution of Very Low-Resolution Face Images Have you ever tried to zoom in on a picture only to have it become pixelated and blurry? That's because the image resolution is too low to support the increased size. Super-resolution techniques aim to improve the resolution of low-resolution images while maintaining the quality and identity of the original image. One such technique is Wavelet-integrated, Identity Preserving, Adversarial network or WIPA. In this article, we will explore the WIPA technology, how it works, and how it can be used to super-resolve very low-resolution face images. What is WIPA? WIPA is a deep learning technique that improves the resolution of very low-resolution face images. It does this by training a neural network to generate high-resolution images from low-resolution images. The neural network consists of two main parts: a baseline CNN network and Wavelet Prediction blocks. The CNN network is responsible for extracting relevant features from the input image, while the Wavelet Prediction blocks predict missing wavelet details in the facial image. How does WIPA work? WIPA works by combining various loss functions to achieve the desired output. The network is trained on large datasets of low-resolution images to learn how to generate a high-resolution image from a low-resolution input. The output image is expected to preserve the identity of the original image while producing fine details that do not exist in the low-resolution input. To achieve this, WIPA uses a combination of perceptual, adversarial, identity, and wavelet prediction loss functions. Perceptual Loss: This loss function is used to maintain the perceived quality of the output image. It compares the high-resolution output with the original high-resolution image using a pre-trained feature-matching network. Adversarial Loss: This loss function is used to ensure that the generated high-resolution images have the same distribution as the real high-resolution images. It trains a discriminator network to distinguish between the generated and real images and encourages the generator network to generate images that mimic the real images. Identity Loss: This loss function is used to preserve the identity of the input image. It compares the high-resolution output image with the low-resolution input image using a pre-trained feature-matching network. Wavelet Prediction Loss: This loss function is used to encourage the network to generate wavelet coefficients that match the original high-resolution images. The training scheme of the Wavelet-Integrated network with the combination of these five loss terms is shown as the WIPA Training Scheme figure. What are datasets used in WIPA? The celebrity dataset was used for training the proposed FH algorithm. The database contains more than 200 K different face images under significant pose, illumination, and expression variations. In our experiment, two distinct groups of 20,000 images are randomly selected from the CelebA dataset as our train and test datasets. To test the generalizing capacity of the method, the performance of the proposed approach is also evaluated on the FW and Helen datasets. How can WIPA be used for super-resolution of very low-resolution face images? To use WIPA for super-resolution of very low-resolution face images, you must first put the training images in the corresponding folders. Then, run the main.py file to train the network and use the test.py file to evaluate the algorithm with different metrics like PSNR, SSIM, and verification rate. The super-resolved images are saved in the “./results/celeba” folder. To run the demo file, use the demo.py file to demonstrate the results of some sample images in the “./sample_images/gt” directory. By default, the images of the “./sample_images/gt” folder will be super-resolved by the wavelet-integrated network by a scale factor of 8, and the results will be saved in the “./sample_images/sr” folder. Conclusion Super-resolution techniques like WIPA are essential for improving the resolution of low-resolution images. WIPA works by training a neural network to generate high-resolution images from low-resolution images. It uses a combination of loss functions to ensure that the output image maintains the original identity, has fine details that do not exist in the low-resolution input, and preserves the perceived quality of the original image. WIPA can be used for super-resolution of very low-resolution face images by putting the training images in the corresponding folders and running the main.py file to train the network.