A GAN-Based Inpainting Framework for Smoke Removal in Critical Safety Imagery

Abstract

Smoke generated during fire and other critical incidents severely degrades the visual quality of captured imagery, hindering safety monitoring, evacuation planning, and firefighting decision-making. Existing smoke- and haze-removal techniques, ranging from prior-based dehazing to single-branch generative models, struggle with thick, structureless smoke and often leave residual artefacts, colour distortion, or require large paired real-world datasets. Across the reviewed literature, prior-based dehazing such as the Dark Channel Prior typically reports Peak Signal-to-Noise Ratio (PSNR) values of only around 13-17 dB under dense haze or smoke, while GAN-based desmoking methods raise this to roughly 18-25 dB yet still report Structural Similarity Index (SSIM) scores below 0.85 and visible residual smoke once density is high, and multi-model pipelines such as DHSGAN incur added computational cost from training two networks separately. This paper proposes a two-stage Generative Adversarial Network (GAN) framework for blind-inpainting-based smoke removal. The framework decouples smoke localization

from image reconstruction through two cooperating sub- networks: a Mask Prediction Network (MPN) that

identifies smoke-affected regions, and a Robust Inpainting Network (RIN) that reconstructs the identified regions using adversarial and VGG16-based perceptual losses. This method reviews the relevant literature, presents GAN fundamentals for completeness, detail the proposed system architecture, network designs, dataset considerations, design goals, and loss formulation, and

position the framework against existing smoke- and haze- removal approaches identified through a structured

literature review. The proposed design is intended as the basis for a subsequent implementation and empirical evaluation.