Summary of Unified-egformer: Exposure Guided Lightweight Transformer For Mixed-exposure Image Enhancement, by Eashan Adhikarla et al.
Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement
by Eashan Adhikarla, Kai Zhang, Rosaura G. VidalMata, Manjushree Aithal, Nikhil Ambha Madhusudhana, John Nicholson, Lichao Sun, Brian D. Davison
First submitted to arxiv on: 18 Jul 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 paper addresses the issue of mixed exposure in image processing, which is crucial in real-world scenarios like surveillance and photography. The authors introduce the Unified-Exposure Guided Transformer (Unified-EGformer), a solution built upon advanced transformer architectures that can handle both overexposure and underexposure. The model features local pixel-level refinement and global refinement blocks for color correction and image-wide adjustments, as well as a guided attention mechanism to identify exposure-compromised regions. The Unified-EGformer is designed to be lightweight, with a memory footprint of only 1134 MB and an inference time of 95 ms, making it suitable for real-time applications like surveillance and autonomous navigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new way to improve images that have both bright and dark parts. The current methods are not good at handling this, so the authors created a new model called Unified-Exposure Guided Transformer (Unified-EGformer). This model uses special blocks to make the image look better and an attention mechanism to focus on the areas that need help. The best part is that it’s fast and doesn’t use too much memory, making it great for real-world applications like security cameras. |
Keywords
» Artificial intelligence » Attention » Inference » Transformer
