Cross-domain heterogeneous residual network for single image super-resolution

Area of Science:

• Computer Vision
• Image Processing
• Deep Learning

Background:

• Single image super-resolution (SISR) is crucial for enhancing low-resolution images.
• Current deep learning methods for SISR often suffer from large model sizes, impacting performance and speed.
• A need exists for efficient and high-performance SISR techniques.

Purpose of the Study:

• To propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction.
• To address the limitations of existing deep learning-based SISR methods regarding performance and speed.
• To improve the quality of super-resolved images through advanced network architecture.

Main Methods:

• Developed a novel heterogeneous residual network employing hierarchical residual learning.
• Incorporated dual-domain enhancement modules for outer residual learning, integrating frequency and space domains.
• Utilized wide-activated residual-in-residual dense blocks for middle residual learning and wide-activated residual attention blocks for inner residual learning.

Main Results:

• The proposed network effectively reconstructs high-quality super-resolved images.
• Evaluations on four benchmark datasets demonstrate state-of-the-art performance.
• The method achieves superior results in terms of image fidelity and reconstruction accuracy.

Conclusions:

• The proposed cross-domain heterogeneous residual network offers a significant advancement in single image super-resolution.
• The hierarchical residual learning strategy effectively models heterogeneous residuals for improved performance.
• The developed method provides a high-performance and efficient solution for super-resolved image reconstruction.