This paper proposes a multi-stage progressive image restoration architecture called MPRNet, which effectively balances spatial details and contextual information in image restoration tasks. The proposed framework consists of three stages: the first two stages use encoder-decoder networks to learn multi-scale contextual information, while the last stage operates on the original image resolution to preserve fine spatial details. A supervised attention module (SAM) is introduced between each pair of stages to refine features and improve performance. Additionally, a cross-stage feature fusion (CSFF) mechanism is added to propagate multi-scale contextualized features between stages, enhancing the information flow and stabilizing the network optimization. MPRNet achieves significant performance gains on ten datasets across various restoration tasks, including image deraining, deblurring, and denoising. It outperforms existing methods in terms of PSNR and SSIM scores, with a 20% relative improvement in PSNR over the previous best method on some datasets. The model is also lightweight and efficient, with fewer parameters and faster inference speed compared to other state-of-the-art methods. The proposed MPRNet is evaluated on multiple image restoration tasks, including image deraining, deblurring, and denoising. It demonstrates superior performance on both synthetic and real-world datasets, achieving state-of-the-art results. The model is also resource-efficient, making it suitable for deployment on devices with limited computational resources. The framework is designed to progressively restore images by incorporating complementary feature processing and flexible information exchange between stages, leading to improved restoration quality and performance.This paper proposes a multi-stage progressive image restoration architecture called MPRNet, which effectively balances spatial details and contextual information in image restoration tasks. The proposed framework consists of three stages: the first two stages use encoder-decoder networks to learn multi-scale contextual information, while the last stage operates on the original image resolution to preserve fine spatial details. A supervised attention module (SAM) is introduced between each pair of stages to refine features and improve performance. Additionally, a cross-stage feature fusion (CSFF) mechanism is added to propagate multi-scale contextualized features between stages, enhancing the information flow and stabilizing the network optimization. MPRNet achieves significant performance gains on ten datasets across various restoration tasks, including image deraining, deblurring, and denoising. It outperforms existing methods in terms of PSNR and SSIM scores, with a 20% relative improvement in PSNR over the previous best method on some datasets. The model is also lightweight and efficient, with fewer parameters and faster inference speed compared to other state-of-the-art methods. The proposed MPRNet is evaluated on multiple image restoration tasks, including image deraining, deblurring, and denoising. It demonstrates superior performance on both synthetic and real-world datasets, achieving state-of-the-art results. The model is also resource-efficient, making it suitable for deployment on devices with limited computational resources. The framework is designed to progressively restore images by incorporating complementary feature processing and flexible information exchange between stages, leading to improved restoration quality and performance.