The field of image and speech super-resolution (SR) is witnessing significant advancements, particularly through the integration of novel methodologies and the enhancement of existing models. A notable trend is the shift towards improving structural fidelity and suppressing spurious details in real-world image SR, leveraging diffusion-based models without the need for additional fine-tuning or external model priors. This approach not only enhances the visual quality but also significantly improves performance metrics such as PSNR and SSIM. Another development is the application of SR as a pre-processing step in remote sensing, particularly for multi-label scene classification tasks. This strategy has been shown to preserve spatial details crucial for accurate classification, thereby improving the performance of downstream tasks. Furthermore, the adoption of transformer-based methods in image SR is reshaping the landscape by overcoming the limitations of previous deep-learning approaches, such as limited receptive fields and poor global context capture. These methods are being combined with traditional networks to balance global and local contexts, offering a promising direction for future research. In the realm of speech SR, the introduction of efficient any-to-48kHz SR systems using Schr"odinger Bridge models represents a leap forward. These systems leverage low-resolution waveforms as informative priors for high-resolution targets, achieving superior sample quality and inference speed with lightweight network backbones.

Noteworthy Papers

• StructSR: Introduces a plug-and-play method enhancing structural fidelity in diffusion-based Real-ISR, significantly improving PSNR and SSIM metrics.
• Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution: Demonstrates the efficacy of SR as a pre-processing step to enhance satellite image quality and improve classification performance.
• State-of-the-Art Transformer Models for Image Super-Resolution: Reviews advancements in transformer-based SR models, highlighting their ability to surpass previous deep-learning approaches.
• Bridge-SR: Presents an efficient any-to-48kHz SR system using Schr"odinger Bridge models, achieving superior sample quality and inference speed.