Reference-Guided Texture Transfer with Deformable Convolutions for Indoor Image Dehazing

Purpose : Image dehazing is a vital image restoration task that aims to recover visual clarity and contrast from images degraded by haze. While much of the existing literature focuses on outdoor scenes such as landscapes or entertainment photography, indoor haze poses a distinct and critical challenge, often caused by smoke in enclosed environments, such as during fire emergencies. In these scenarios, faithful restoration is paramount: the reconstructed image must remain as close as possible to the ground truth, without introducing artificial textures or unrealistic elements, since accuracy can directly impact decision-making. Methods : In this paper, we propose DTTN, an enhanced transformer-based framework that advances our earlier Texture Transfer Dehazing Network (TTDN) by incorporating deformable convolutions and a streamlined architecture designed to better capture nonlocal dependencies and spatially adaptive features. DTTN retains the core innovation of the Reference Super-Resolution (RefSR) paradigm by leveraging a high-quality reference image to guide the reconstruction of fine textures in hazy images. Our pipeline extracts deep features from both the input hazy image and a clean high-resolution reference using a modified VGG19 backbone, transforming them into patch-based representations. Using Deformable Convolutional Networks (DCNs), the model dynamically aligns relevant textures from the reference, enhancing spatial correspondence. Furthermore, we introduce a Gradient Density Enhancement Module, which leverages edge and structural cues to further improve restoration fidelity. Results : We evaluated DTTN on the RESIDE-Indoor dataset, reporting new benchmark results. Quantitative evaluations demonstrate that DTTN achieves or exceeds state-of-the-art performance on standard metrics such as PSNR and SSIM, while also improving computational efficiency via optimized patch sizes and strides in the texture transfer module. Qualitative comparisons highlight the ability of DTTN to preserve fine textures and structural consistency across a wide range of indoor haze scenarios. Conclusion : Overall, our findings highlight DTTN as an effective and efficient solution for faithful indoor image dehazing, with strong potential for deployment in safety-critical vision applications.