Image enhancement and restoration are important tasks in many domains, such as photography, medical imaging, security, and forensics. However, manually editing or repairing images can be time-consuming and tedious. What if you could train an AI model to do it for you? In this article, you will learn the basics of how to train an AI model to restore images using deep learning techniques.
Image restoration is the process of recovering an image that has been degraded by some factors, such as noise, blur, distortion, or missing pixels. The goal is to restore the image to its original or desired quality, or to enhance some features of interest. Image restoration can be seen as a form of inverse problem, where you try to find the unknown original image from the observed degraded image.
Image restoration is the process of improving the quality of a digital image, which are degraded or corrupted over time. Techniques involved are mathematical algorithms and signal processing methods etc This process is widely used in various fields, including photography, medical imaging, satellite imagery, and digital forensics.
Image enhancement and restoration are crucial in fields like photography, medicine, security, and forensics. Manual image editing is time-consuming. This article explores training AI models for image restoration. Image restoration aims to recover degraded images due to factors like noise, blur, or distortion. It's akin to solving an inverse problem, seeking the original image from its degraded version.
Image restoration isn't new. There are many image enhancement techniques already used in digital imaging including infrared scanning and using pixels around a damaged area to make a best guess on what values the pixels might have. These are often called "scratch and dust removal" and are effective tools for small errors. However, they rely on a single image to make those guesses which limits their accuracy.
Deep learning is a branch of AI that uses neural networks to learn from data and perform complex tasks. Neural networks are composed of layers of artificial neurons that can process and transform information. By adjusting the weights and biases of the neurons, the network can learn to approximate any function or model any relationship. Deep learning can help with image restoration because it can learn to extract and reconstruct the relevant features of an image from a large amount of data, without relying on predefined rules or assumptions.
Deep learning, a subset of AI, harnesses neural networks to tackle intricate tasks through data-driven learning. These neural networks consist of layers of artificial neurons that process and manipulate data. By fine-tuning the weights and biases of these neurons, the network can master approximating functions or modeling relationships, all without the need for predefined rules or assumptions. The beauty of deep learning in the realm of image restoration lies in its ability to decipher and reconstruct image features. It accomplishes this by sifting through vast data, enabling it to restore images without being constrained by rigid predefined guidelines.
Computer Vision uses Deep Learning with image (and video) data for various tasks like image classification, segmentation, generation, and restoration. Convolution Neural Networks (CNNs) are specially designed neural network that learns low and high-dimensional image features (kernels). The function of these features ranges from detecting edges in an image to the presence of faces in an image, and much more. The advantage of using deep learning over other methods is the nature of end-to-end learning on images that leads to statistically learned features. These are shown to be more valuable for computer vision tasks, such as image restoration, than manually creating these features using older non-learning-based statistical algorithms.
Deep learning helps to improve the "guesses" that computers use to fill in the blanks where image data is missing. Those areas could be color accuracy, missing parts (e.g. scratching or dust) or lighting issues.
Image restoration using deep learning can be achieved through a variety of methods and architectures, with autoencoders, generative adversarial networks (GANs), and convolutional neural networks (CNNs) being the most popular. Autoencoders encode an input image into a lower-dimensional representation, then decode it back to an output image that can be the same or enhanced. GANs consist of a generator and a discriminator that compete with each other, leading to the generator producing high-quality images. Lastly, CNNs use convolutional layers to extract features from images for tasks such as denoising or deblurring.
Convolution Neural Networks (CNNs) are the cornerstone of every computer vision task. Image Restoration may involve more complex neural network architectures built on top of CNNs such as Generative Adversarial Networks (GANs), AutoEncoders, Transformers, Diffusion Models, and more. Image Diffusion Models (such as Stable Diffusion, Midjourney) work by introducing a noise (eg. Gaussian) to an image and then using a neural network to learn that noise to de-noise that noisy image.
Image restoration with deep learning involves key methods like autoencoders, which compress and reconstruct images for enhancement. Generative Adversarial Networks (GANs) consist of a generator and discriminator competing to produce high-quality images. Convolutional Neural Networks (CNNs) use convolutional layers to extract features for tasks like denoising or deblurring.
For training an AI model for image restoration, three main ingredients are necessary: data, network, and loss function. For data, you need a large and diverse dataset of images that can provide input and output for your model. Depending on the task, you may need pairs of degraded and restored images or only degraded images if using a GAN. Preprocessing the data, such as resizing, cropping, augmenting, or normalizing it may also be necessary. For the network, pick an existing network or modify it to suit your needs. You may also create a network from scratch with layers such as convolutional, deconvolutional, residual or attention. For the loss function, you need to define one that can measure how well your model performs the task. The loss function compares the output of your model with the desired output and calculates a numerical value that represents the error or discrepancy. The lower the loss, the better the model. Standard loss functions include mean squared error or cross entropy, while custom loss functions include perceptual loss or adversarial loss. To train your model, feed your data to your network and use an optimization algorithm such as gradient descent or Adam to update the weights and biases of your network based on the loss function. Repeat this process for many iterations or epochs until your model reaches satisfactory performance.
One of the famous methods of restoring images is by using a Diffusion Model. The process of training a Diffusion Model involves encoder, denoiser, and decoder models. The encoder model is trained to learn a representation of the image. Then some noise is added to this image vector representation. The denoiser model is trained to learn the noise present in the image. We remove this noise from our encoded (noisy) image representation, in order to get our approximation for encoded de-noised image. Then, we train a decoder model to inverse the image representation back into an estimate for our original image. We iterate over this learning process using deep learning (and gradient descent) to improve the performance of our diffusion model.
Training an AI model for image restoration is akin to crafting a recipe. Your data is the fresh ingredients, the network is the chef, and the loss function serves as the taste test. Like refining a culinary creation, you iterate and adjust until your model reaches its desired level of performance.
To evaluate an AI model for image restoration, you need to test it on new or unseen images, and measure its performance using metrics and visual inspection. Metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), or inception score (IS) can quantify the quality or similarity of the restored images with the original or desired images. However, these metrics may not always reflect the human perception of the images, and may have limitations or biases. Visual inspection is also important for assessing the visual quality and realism of the restored images. Look for artifacts, distortions, blurriness, or inconsistencies in the images, and compare them with the original or desired images. Additionally, solicit feedback from other people or experts to validate your evaluation.
Evaluating an AI model for image restoration involves testing it on new images and using both metrics and visual inspection. Metrics like PSNR, SSIM, and IS help quantify image quality, but may not capture human perception. Visual inspection is crucial to check for artifacts, distortions, and overall realism. Seek feedback from others, including experts, to ensure a comprehensive evaluation.
Using generative AI to repair images may work better when you have many images of the same scene or person to work from (e.g. video) as fixing single frames would be easier to guess for the AI engine. Having a single original may limit the effectiveness of a generative AI and other, existing tools may be more effective.