The rapid advancements in digital imaging technologies have created a growing demand for effective image restoration techniques. Various kinds of degradation, including noise, blur, and low resolution, should be handled with these techniques. Restoration is important in many applications, including medical imaging, surveillance, photography, and remote sensing, where image quality will be critical to the correctness of analysis and decision. This article provides an all-inclusive review of state-of-the-art (SOTA) methods
in image restoration, covering traditional methods as well as modern techniques like deep learning and transformer-based models. Traditional image restoration techniques include deblurring, denoising, and super-resolution based on mathematical models and handcrafted algorithms. These methods were indeed effective for certain types of noise or blur but generalized poorly to various real-world scenarios. Recent advances in machine learning (ML), especially deep learning (DL) using convolutional neural networks (CNNs), have made data-driven approaches that learn directly from large datasets much more effective. Recently, transformer-based models-Vision Transformers and Swin Transformers-have shown the ability to capture global dependencies in images, leading to superior performance on complex restoration tasks. It also mentions the challenge of generalization across the type of degradation, say mixed noise or blur, and across different datasets. The proposed survey indicates the limitations of existing approaches, including computational cost and generalization challenges, and offers insights into possible directions for future
research. Considering these challenges and achievements, this article attempts to provide helpful guidance on methods for future research on restoring images.
Key words: Image restoration, Deep learning, Transformer-based architectures, Noise reduction, Cross-domain models.
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