Image Restoration: A Comprehensive Review

In this paper, researchers propose a novel approach to image restoration using attention retractable transformer (ART). ART is a deep learning model that can effectively restore images degraded by various factors such as noise, blur, and compression. The key innovation of ART is the introduction of attention mechanisms, which allow the model to selectively focus on specific parts of the image during restoration.
The authors begin by discussing the challenges of image restoration and the limitations of traditional methods. They argue that these approaches often rely on heuristics or manual editing, which can be time-consuming and may not produce optimal results. To address this problem, they propose ART, a transformer-based model that leverages attention mechanisms to improve image restoration.
The ART model consists of three main components: the encoder, the decoder, and the attention mechanism. The encoder takes the degraded image as input and generates a latent representation of the image. The decoder then uses this latent representation to generate an estimated clean image. Finally, the attention mechanism is used to refine the estimated image by selectively focusing on specific parts of the image.
The attention mechanism in ART is based on a novel technique called "attention retractable transformer." This technique allows the model to adaptively adjust the level of attention based on the complexity of the task at hand. In simple tasks, the attention mechanism can be collapsed to a single "global" attention, allowing the model to focus on the entire image. In more complex tasks, the attention mechanism can be expanded to multiple "local" attendances, allowing the model to focus on specific parts of the image.
The authors evaluate ART on several benchmark datasets and show that it outperforms state-of-the-art methods in terms of both objective metrics (e.g., peak signal-to-noise ratio) and subjective quality assessments. They also perform a series of ablation studies to analyze the contribution of different components of ART, showing that the attention mechanism is essential for its performance.
In conclusion, this paper presents a novel approach to image restoration using attention retractable transformer. The proposed model leverages attention mechanisms to improve the accuracy and efficiency of image restoration tasks, and outperforms state-of-the-art methods in both objective and subjective evaluations.