The field of image super-resolution and editing is rapidly advancing, with a focus on developing efficient and effective methods for improving image quality. Recent developments have centered around the use of generative models, knowledge distillation, and novel loss functions to enhance image resolution and reduce computational costs. Notably, researchers are exploring the use of ultra-lightweight models, conditional pseudo-supervised contrast, and student discriminator assisted knowledge distillation to improve performance and efficiency. These innovations have significant implications for edge-device applications and real-world image processing.

Noteworthy papers include: PocketSR, which introduces an ultra-lightweight model for real-world image super-resolution, achieving remarkable speedup and performance on par with state-of-the-art models. Conditional Pseudo-Supervised Contrast for Data-Free Knowledge Distillation proposes a novel learning paradigm for data-free knowledge distillation, improving category-wise diversity and performance. SDAKD presents a novel GAN distillation methodology that introduces a student discriminator to mitigate capacity mismatch, demonstrating consistent improvements over baselines and SOTA methods. Improving the Spatial Resolution of GONG Solar Images to GST Quality Using Deep Learning employs a GAN-based super-resolution approach to enhance low-resolution solar images, effectively recovering fine details and achieving high reconstruction quality. Efficient High-Resolution Image Editing with Hallucination-Aware Loss and Adaptive Tiling presents MobilePicasso, a novel system for efficient image editing at high resolutions, minimizing computational cost and memory usage while improving image quality and reducing hallucinations.