The field of image restoration and super-resolution is rapidly evolving, with a focus on developing efficient and effective methods for improving image quality. Recent research has explored the use of diffusion-based models, attention mechanisms, and frequency-domain processing to achieve state-of-the-art results. Notably, the use of hierarchical mixture of sparse attention and post-training paradigms has led to significant improvements in image super-resolution. Additionally, the development of benchmarks and evaluation frameworks for dynamic 3D Gaussian splatting and pore-scale facial trajectory tracking has advanced the field of facial image analysis. Some noteworthy papers in this area include HIMOSA, which proposes a lightweight super-resolution framework for remote sensing imagery, and IRPO, which introduces a low-level GRPO-based post-training paradigm for image restoration. Furthermore, papers like FRAMER and ResDiT have made significant contributions to the development of efficient and effective image super-resolution methods. Overall, the field is moving towards the development of more efficient, effective, and scalable methods for image restoration and super-resolution.
Advancements in Image Restoration and Super-Resolution
Sources
HIMOSA: Efficient Remote Sensing Image Super-Resolution with Hierarchical Mixture of Sparse Attention
Pore-scale Image Patch Dataset and A Comparative Evaluation of Pore-scale Facial Features
IRPO: Boosting Image Restoration via Post-training GRPO
FRAMER: Frequency-Aligned Self-Distillation with Adaptive Modulation Leveraging Diffusion Priors for Real-World Image Super-Resolution
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
Two-Stage Vision Transformer for Image Restoration: Colorization Pretraining + Residual Upsampling
PoreTrack3D: A Benchmark for Dynamic 3D Gaussian Splatting in Pore-Scale Facial Trajectory Tracking
PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution
Does Head Pose Correction Improve Biometric Facial Recognition?
AaPE: Aliasing-aware Patch Embedding for Self-Supervised Audio Representation Learning
Beyond the Ground Truth: Enhanced Supervision for Image Restoration
Learning Single-Image Super-Resolution in the JPEG Compressed Domain
UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers
OmniScaleSR: Unleashing Scale-Controlled Diffusion Prior for Faithful and Realistic Arbitrary-Scale Image Super-Resolution
Semantic-Guided Two-Stage GAN for Face Inpainting with Hybrid Perceptual Encoding
EvoIR: Towards All-in-One Image Restoration via Evolutionary Frequency Modulation