SPICE: Simple and Practical Image Clarification and Enhancement
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Abstract (arXiv)
We introduce a simple and efficient method to enhance and clarify images. More specifically, we deal with low light image enhancement and clarification of hazy imagery (hazy/foggy images, images containing sand dust, and underwater images). Our method involves constructing an image filter to simulate low-light or hazy conditions and deriving approximate reverse filters to minimize distortions in the enhanced images. Experimental results show that our approach is highly competitive and often surpasses state-of-the-art techniques in handling extremely dark images and in enhancing hazy images. A key advantage of our approach lies in its simplicity: Our method is implementable with just a few lines of MATLAB code.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 9 Oct 2025.
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References · 8
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
- 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 541
- 2019UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environmentscited by 84
- 2024LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Modelscited by 70
- 2024Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representationscited by 21
- 2021A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learningcited by 19
- 2024LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusioncited by 12
- 2017Zero-order Reverse Filteringcited by 8
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