With the significant advancement in the field of Artificial Intelligence, the sub-fields of AI, including Natural Language Processing, Natural Language Understanding, Computer Vision, etc., are also improving at a fast pace. In the realm of computer vision and image processing, picture restoration is an important task. Its main objective is to recreate a high-quality image from a low- or degraded-quality observation. Noise, blur, or downscaling are just a few of the variables that might lead to this degradation. Traditional image restoration challenges have a well-defined and straightforward degradation process that frequently follows well-known patterns like Gaussian noise or bicubic downsampling. Many algorithms have been created for these particular situations, leading to considerable improvements in image restoration.
These conventional techniques have drawbacks, chiefly due to their inability to generalize to situations in real life where the deterioration is intricate and unknowable. This is where the promising study area of blind image restoration (BIR) comes into play. BIR is not restricted to particular settings and tries to take up the problem of restoring images with generic degradations. It has practical applications, such as repairing old photographs or videos, and broadens the scope of traditional picture restoration jobs. Existing BIR methods face three critical challenges –
- Achieving realistic image reconstruction
- Handling general images with various types of degradations
- Addressing extreme degradation cases
In recent research, a team of researchers has introduced a unique approach called DiffBIR, which addresses the blind image restoration problem. This approach tries to restore images without being aware of the precise deterioration they have endured. Their pipeline consists of two stages and uses pretrained text-to-image diffusion models. The initial stage is the restoration module pretraining. The team has focused on pretraining a restoration module that can manage a wide variety of varied degradations. The model’s capacity to generalize in situations where images can be damaged in a variety of ways will be much improved by completing this phase. They basically teach the model how to spot and correct common image degradations like noise, blur, and other types of distortion.
The team has taken advantage of generating powers of latent diffusion models in the second step. To create visuals from text descriptions, these models are trained beforehand. They can be adjusted to provide realistic restored images when used in the context of image restoration. The team has presented LAControlNet as an injective modulation sub-network to help with this. The pretrained Stable Diffusion model is fine-tuned to the specific objective of picture restoration using this sub-network.
A customizable module has also been developed to allow consumers additional control over the trade-off between image quality and fidelity. Users of this module can change how these two factors are balanced during the inference denoising process. Users can alter the restoration outcomes by adding latent image advice to match their preferences. In thorough testing, the team has discovered that their DiffBIR framework outperformed cutting-edge techniques for blind picture super-resolution and blind face restoration. The model’s effectiveness and superiority in handling challenging real-world image restoration problems were demonstrated in these studies, which used both synthetic and real-world datasets.
In conclusion, DiffBIR is a method that efficiently addresses the blind image restoration problem by combining pretrained text-to-image diffusion models, a two-stage pipeline, and a configurable module. The discipline of computer vision and image processing has benefited greatly from its outstanding performance in blind picture super-resolution and blind face restoration.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
