Summary of Gaussiansr: High Fidelity 2d Gaussian Splatting For Arbitrary-scale Image Super-resolution, by Jintong Hu et al.
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution
by Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, Lei Zhang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors propose a novel arbitrary-scale super-resolution (ASSR) method called GaussianSR, which leverages continuous Gaussian fields to represent pixels. Unlike traditional approaches that treat pixels as discrete points, GaussianSR establishes long-range dependencies by rendering mutually stacked Gaussian fields, enhancing representation ability. The encoded features are simultaneously refined and upsampled through this process. A classifier is developed to dynamically assign Gaussian kernels to all pixels, further improving flexibility. All components of GaussianSR (encoder, classifier, Gaussian kernels, and decoder) are jointly learned end-to-end. Experimental results demonstrate that GaussianSR achieves superior ASSR performance with fewer parameters than existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GaussianSR is a new way to make blurry images clear again. It works by looking at each pixel in the image as not just a single point, but as a whole field of possibilities. This helps it figure out how to fix bigger problems in the image, like blurry edges or missing details. The authors also added a special tool that helps adjust the way the image is fixed based on what’s most important. All of this happens automatically, and it makes the images much clearer with fewer computer resources needed. |
Keywords
» Artificial intelligence » Decoder » Encoder » Super resolution
