https://jjcit.org/paper/260 10.5455/jjcit.71-1735034495 Nikita Singhal,Anup Kadam,Pravesh Kumar,Hritik Singh,Aaryan Thakur,Pranay Image restoration,Deep learning,Transformer-based architectures,Noise reduction,Cross-domain models 3 10641 1561 24-Dec.-2024 22-Feb.-2025 26-Mar.-2025 The rapid advancements in digital imaging technologies, including image restoration (IR), 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 (DL) 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 DL using convolutional neural networks (CNNs), have made data-driven approaches that learn directly from large datasets much more effective. Recently, transformer-based models, such as Vision Transformers and Swin Transformers, have shown the ability to capture global dependencies in images, leading to superior performance on complex restoration tasks. It is also to mention 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.