In the contemporary world, the most important aspect of an image is the quality. It is very important especially for people who work with images. Photographers or graphic designers, and even social media influencers for clear and high resolution images. At Kraftors Web Solutions, we understand this need and are proud to introduce our latest SaaS product: AI Image Enhancer. The current AI technology serves as the base for developing the Image Enhancer. The goal is to covert low-quality images to high quality images. This blog will ensure you to get the overall picture of what this tool is and how it works.
Image Enhancement using GANs
The Image Enhancer implementation is one of the highest levels based on Generative Adversarial Networks (GANs). GANs are a type of neural network architecture consists of generator and the discriminator. The generator produce clear images from the low-quality inputs and discriminator determines how realistic the generated images are.
We use the DIV2K Dataset for training.
The DIV2K dataset, containing 2000 high-resolution images, was used for training the model. We reduced the size of the above images by a factor of 4 to obtain low-resolution inputs for the system. Our model learns the best methods to reconstruct high-resolution images from 4x down-scaled images.
Leveraging CNNs for Image Enhancement
Convolutional Neural Networks (CNNs) form the backbone of our generator model. CNNs are particularly well-suited for image processing tasks due to their ability to capture spatial hierarchies in images. In our generator, residual blocks and attention mechanisms focus on critical regions of the image, enhancing important details.
Residual Blocks and Attention Mechanisms
Residual blocks help in preserving the quality of the image by allowing the network to learn residual functions. The network learns the difference between low-resolution and high-resolution images instead of directly mapping them and improve the output quality. Attention mechanisms improve the process by enabling the model to focus on crucial image parts. And preventing the loss of key details.
Discriminator and Adversarial Loss
The discriminator in our GAN architecture is also based on CNNs. Its primary role is to evaluate the realism of the images generated by the generator. The generator and discriminator are trained continuously to enhance the quality of generated images using adversarial loss. The generator aims to generate images that the discriminator can’t distinguish from real high-resolution images. That resulting in progressively better image enhancements.
Exploring RNNs for Temporal Dependencies
While our primary focus remains on CNNs for image processing, we also explore the use of Recurrent Neural Networks (RNNs) for capturing temporal dependencies in sequential data. This exploration allows us to enhance images in a way that maintains consistency across sequences, making our Image Enhancer versatile for various applications, including video enhancement.
Ensuring High-Level Feature Fidelity with Perceptual Loss Functions
To ensure that our enhanced images not only look good but also retain high-level features, we use perceptual loss functions during training. These loss functions help in preserving the visual appeal and feature fidelity of the enhanced images. By comparing high-level features extracted from a pre-trained network, our model learns to produce images that are both visually appealing and faithful to the original content
Optimizing with Adam
Our model’s training process is optimized using the Adam optimization algorithm, which is known for its efficiency and effectiveness in training deep learning models. It adapts the learning rate for each parameter, ensuring faster convergence and better performance
Evaluating Performance with PSNR and SSIM
To guarantee the highest quality results, we rigorously evaluate our model’s performance using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). These metrics provide a quantitative measure of the quality of the enhanced images, ensuring that our Image Enhancer consistently delivers top-notch results.
Conclusion
Kraftors’ Image Enhancer is a testament to our commitment to leveraging advanced AI technology to meet the needs of our users. By integrating GANs, CNNs, RNNs, and sophisticated loss functions, we’ve created a tool that can transform low-resolution images into stunning high-resolution visuals. If you’re a professional photographer, a graphic designer or value high-quality images, our AI Image Enhancer is designed to cater to your needs. Try it out today and see the difference for yourself.
For a deeper dive into the features and benefits of our Image Enhancer, check out our previous blog post that is Best AI Image Enhancer FREE, where we explore its capabilities in detail.
