Summary of Latent Modulated Function For Computational Optimal Continuous Image Representation, by Zongyao He et al.
Latent Modulated Function for Computational Optimal Continuous Image Representation
by Zongyao He, Zhi Jin
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in Arbitrary-Scale Super-Resolution (ASSR) have led to the development of Local Implicit Image Function (LIIF) and Implicit Neural Representation (INR) based methods, which utilize Multi-Layer Perceptron (MLP) decoding. However, these continuous image representations typically require decoding in High-Resolution (HR) High-Dimensional (HD) space, resulting in a quadratic increase in computational cost, hindering practical applications of ASSR. To address this challenge, the authors propose Latent Modulated Function (LMF), which decouples HR-HD decoding into shared latent decoding and independent rendering in HR Low-Dimensional (LD) space, achieving a computationally optimal paradigm for continuous image representation. Specifically, LMF uses an HD MLP to generate latent modulations of each LR feature vector, enabling a modulated LD MLP to adapt to any input feature vector and perform rendering at arbitrary resolution. Furthermore, the authors design Controllable Multi-Scale Rendering (CMSR) algorithm, allowing adjustments in decoding efficiency based on rendering precision. The results demonstrate that converting existing INR-based ASSR methods to LMF can reduce computational cost by up to 99.9%, accelerate inference by up to 57 times, and save parameters by up to 76% while maintaining competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make image super-resolution more efficient. Right now, computers need to process lots of information to make low-quality images look better. This makes it hard for them to do this quickly or use less power. The authors came up with a new method called Latent Modulated Function (LMF) that breaks down the processing into two parts: one part is easy and fast, and the other part can be done more slowly if needed. They also created an algorithm that lets computers adjust how much effort they put into making images look better based on how important it is to get them right. The results show that this new method makes super-resolution faster and uses less power while still giving good results. |
Keywords
» Artificial intelligence » Inference » Precision » Super resolution
