Residual Dense Network for Image Restoration (1812.10477v2)

Abstract: Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in IR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via densely connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory mechanism. To adaptively learn more effective features from preceding and current local features and stabilize the training of wider network, we proposed local feature fusion in RDB. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. We demonstrate the effectiveness of RDN with several representative IR applications, single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring. Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually.

• The paper introduces a Residual Dense Network (RDN) that fully exploits hierarchical features using innovative Residual Dense Blocks.
• It leverages local and global feature fusion to improve information flow and stability, significantly boosting restoration performance.
• The model achieves state-of-the-art PSNR and SSIM improvements, demonstrating versatility across tasks such as super-resolution, denoising, artifact reduction, and deblurring.

Analysis of Residual Dense Network for Image Restoration

The paper "Residual Dense Network for Image Restoration" presents an advanced model aimed at addressing various challenges in image restoration tasks, including super-resolution, denoising, compression artifact reduction, and deblurring. The authors introduce the Residual Dense Network (RDN), which leverages residual dense blocks (RDBs) to maximize the utilization of hierarchical features across all network layers, ensuring improved efficiency and effectiveness.

Key Contributions

The primary contribution of this work is the development of the RDN framework, which fully exploits the hierarchical features extracted from low-quality (LQ) images through innovative dense connectivity. This section elucidates the main technical aspects and the implications of the proposed model:

1. Residual Dense Block (RDB): Central to the RDN architecture is the RDB, which utilizes densely connected Convolutional Neural Network layers to enhance feature learning. The RDB incorporates a contiguous memory mechanism, allowing seamless feature transfer and improving information flow.
2. Local and Global Feature Fusion: The paper introduces local feature fusion (LFF) within each RDB and global feature fusion (GFF) across all RDBs. These mechanisms ensure the effective amalgamation of both local and global hierarchical features, optimizing learning and enabling stable training of wider networks.
3. Adaptability: The RDN framework is adaptable across different image restoration tasks. It achieves state-of-the-art performance on single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring tasks.

Results and Performance

Experimental results substantiate the RDN's superior performance across various benchmarks. The RDN surpasses existing methods like EDSR and MemNet, showing notable improvements in PSNR and SSIM metrics across datasets such as Set5, Set14, and B100, for diverse scaling factors. Its robustness is further evidenced in tasks with challenging degradation models like BD and DN, demonstrating the model's ability to recover detailed textures and reduce artifacts effectively.

Implications and Future Directions

The implications of this research are significant both theoretically and practically:

• Theoretical Insights: The work provides insights into the benefits of fully utilizing hierarchical features in deep networks. The contiguous memory mechanism and feature fusion techniques present novel contributions to the understanding of deep learning architectures for image restoration.
• Practical Applications: The enhanced performance and adaptability of the RDN model have direct implications for improving tools used in real-world applications, ranging from medical imaging to security surveillance.
• Future Prospects: The robust architecture of RDN opens avenues for further exploration in complex image processing tasks. Future research could explore its integration with adversarial networks or its application in other domains such as video restoration and enhancement.

Overall, this paper makes a substantial advancement in image restoration techniques, presenting a versatile framework capable of meeting the demands of various restoration challenges while setting a foundation for future studies to build upon.