Integrating multi-scale attention network with deep image prior for single image restoration

Deep learning models, which offer more powerful image restoration capabilities, have resulted in substantial advances in the area of image restoration. However, there remains ample opportunity for further research due to the inherent complexity of these models and the limitations each one faces in accurately recovering images. Current state-of-the-art methods typically depend on extensive datasets and require prior images for training to restore the original images. The proposed method uses the concept of deep image prior (DIP), which posits that neural networks can serve as effective image priors for supervised learning without needing vast amounts of data. DIP is undeniably effective, yet it does have some limitations. One notable challenge is that the network used in DIP may struggle to capture minute details in images, potentially leading to a loss of crucial information. Additionally, there’s a risk of overfitting. To address these limitations, a novel approach is proposed that integrates a multi-scale attention block with dilated convolution into the neural network architecture within the Deep Image Prior framework. This innovative solution aims to enhance the network's ability to capture fine details while mitigating overfitting. We validate our approach through experiments on three distinct tasks: image denoising, image deblurring, and JPEG compression artifact removal. To assess the effectiveness of the proposed method, we employ quantitative metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR), Structural similarity index (SSIM), and Multi-scale structural similarity index (MS-SSIM).The proposed architecture not only significantly improves the image restoration process but also mitigates the problem of overfitting compared to the existing methods.