The field of image and video enhancement is moving towards developing more efficient and effective methods for improving the quality of low-resolution or degraded visual content. Researchers are exploring innovative approaches that combine advanced techniques such as super-resolution, feature upsampling, and neural networks to achieve state-of-the-art results. Noteworthy papers in this area include Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN, which proposes a method for improved object detection from low-resolution images. Another notable paper is MambaVSR, which introduces a content-aware scanning state space model for video super-resolution. Additionally, FADPNet and FGA-NN demonstrate promising results in face super-resolution and film grain analysis, respectively. MSNeRV also shows impressive performance in neural video representation with multi-scale feature fusion.