SPICE: Simple and Practical Image Clarification and Enhancement

2025-10-09

AI Summary

This work addresses joint enhancement of low-light images degraded by multiple physical factors—including haze, dust, and underwater scattering—by proposing a lightweight, physics-driven inverse filtering method. The approach explicitly models image degradation via an analytically tractable filter and designs its approximate inverse to enable end-to-end distortion-suppressing enhancement. Unlike deep learning–based methods, it requires only minimal MATLAB implementation, no neural network architecture, and no large-scale training, ensuring high reproducibility and suitability for edge deployment. Quantitative and visual evaluations on extreme low-light and complex hazy scenes demonstrate performance at or beyond state-of-the-art methods, with significant improvements in contrast, structural detail preservation, and color fidelity. Results validate the effectiveness of compact, physically grounded models for realistic degradation modeling.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Enhancing low light images and clarifying hazy imagery
• Constructing reverse filters to minimize image distortions
• Providing simple implementation with minimal MATLAB code

Innovation

• Constructs filters to simulate poor conditions
• Derives reverse filters to minimize image distortions
• Implemented simply with few MATLAB code lines