Transforming Blurry Photos into Sharp and High-Definition Images with AI
Looking at old photos, it’s clear that there’s a big difference compared to the high-quality images produced by modern cameras. Old photos used to be blurry and pixelated, lacking the sharpness and detail we expect today. However, even with the latest cameras, we can still encounter issues like blurriness depending on the camera settings or environment. This raises the question: Can we transform blurry pictures into sharp and high-definition ones? The answer lies in blind face restoration (BFR), a technique that reconstructs clear and faithful images of a person’s face from degraded or low-quality input images.
BFR has garnered significant attention in image processing and computer vision due to its practical applications in surveillance, biometrics, and social media. Recently, deep learning methods, which rely on artificial neural networks, have shown great promise in blind face restoration. These methods can learn complex mappings from data without the need for hand-crafted features or explicit modeling of the degradation process.
These techniques employ various metrics, formulations, and parameters to enhance restoration quality. They often use the L1 training loss to ensure fidelity. Additionally, adversarial loss and perceptual loss have been introduced to achieve more realistic results. Some approaches also utilize face-specific priors like face landmarks, facial components, and generative priors. However, combining all these constraints can lead to complicated training processes that require extensive hyper-parameter tuning. Moreover, the instability of adversarial loss makes the training more challenging.
To overcome these issues, a novel method called DifFace has been developed. DifFace can handle complex and unseen degradations more effectively than existing techniques without the need for complicated loss designs. The key lies in the posterior distribution from the low-quality (LQ) image to its high-quality (HQ) counterpart. The method uses a transition distribution from the LQ image to an intermediate state obtained through a pre-trained diffusion model. This intermediate state is then gradually transmitted to the HQ target using a recursive application of the diffusion model.
DifFace offers several advantages. It is more efficient than the full reverse diffusion process, as a pre-trained diffusion model can be utilized. There is no need to retrain the diffusion model from scratch, and multiple constraints in training are not required. Despite these simplifications, DifFace can handle unknown and complex degradations successfully. Comparisons with state-of-the-art techniques have shown that DifFace produces high-quality and sharp images with fine details from low-quality and blurred input images.
In summary, DifFace provides a novel framework to address the Blind Face Restoration problem. For more information, check out the Paper and Github links. Credit goes to the researchers involved in this project. Join our Reddit page and discord channel to stay updated on the latest AI research news and cool projects.
About the Author:
Daniele Lorenzi is a Ph.D. candidate at the Institute of Information Technology (ITEC) at Alpen-Adria-Universität (AAU) Klagenfurt. He is currently working in the Christian Doppler Laboratory ATHENA and his research interests include adaptive video streaming, immersive media, machine learning, and QoS/QoE evaluation.