When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …

Abstract We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image
restoration method that harnesses generative prior and the power of model scaling up …

Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely
applied in low-level vision tasks, like image super-resolution (SR). As a classic regression …

Self-supervised pretraining has achieved remarkable success in high-level vision, but its
application in low-level vision remains ambiguous and not well-established. What is the …

Super-resolution (SR) techniques designed for real-world applications commonly encounter
two primary challenges: generalization performance and restoration accuracy. We …

Short-form UGC video platforms like Kwai and TikTok have been an emerging and
irreplaceable mainstream media form thriving on user-friendly engagement and …

While single task image restoration (IR) has achieved significant successes, it remains a
challenging issue to train a single model which can tackle multiple IR tasks. In this work, we …

Traditional single-task image restoration methods excel in handling specific degradation
types but struggle with multiple degradations. To address this limitation, we propose …

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR)
performances in recent years. While most existing work assumes a simple and fixed …

Performance and generalization ability are two important aspects to evaluate the deep
learning models. However, research on the generalization ability of Super-Resolution (SR) …