Zero-Shot Benchmarking of Unified Multimodal Models for Shallow-Water UAV Image Distortion Assessment and Restoration

Accurate mapping of shallow-water environments using unmanned aerial vehicle (UAV) imagery is challenged by water-induced distortions, including refraction, caustics, and wavelength-dependent attenuation. The scarcity of paired distorted and reference imagery further limits the development of supervised restoration methods. To address this challenge, we introduce KAUSTics, a semi-synthetic dataset combining 1,280 reference patches derived from 32 high-resolution UAV images with 12,800 degraded observations generated through Blender-based water-surface rendering and empirical color and blur augmentations. We further propose KAUSTics-Net, a pixel-space conditional diffusion framework integrating a multi-scale conditional encoder, feature-wise linear modulation (FiLM), cross-attention, and a composite reconstruction objective to restore shallow-water imagery. We evaluated the framework against eight image-restoration methods, complemented by architectural ablations and cross-dataset experiments. On KAUSTics, KAUSTics-Net achieved a mean peak signal-to-noise ratio (PSNR) of 31.715 +/- 0.439 dB, structural similarity index (SSIM) of 0.849 +/- 0.002, and learned perceptual image patch similarity (LPIPS) of 0.145 +/- 0.007 across three training seeds. On the independent Sea-Undistort dataset, sampler-aware fine-tuning improved restoration fidelity, reaching 19.469 +/- 0.332 dB PSNR and 0.554 +/- 0.005 SSIM under combined water-surface distortions, although perceptual trade-offs remained. Additional experiments on real UAV imagery examined feature preservation and segmentation behavior. These findings demonstrate the potential of semi-synthetic training and conditional diffusion for shallow-water image restoration while highlighting the importance of evaluating both reconstruction fidelity and downstream remote-sensing utility.

Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. This paper describes a systematic evaluation pipeline for underwater reconstruction, which can be used to assess a method for accuracy; consistency of reconstruction over camera moves; and the effect of water parameters. We use this pipeline to evaluate a range of current procedures, from models constructed using explicit but approximate physical models of scattering to Vision-Language Models (VLMs which are not currently trained with explicit physical models). Overall, VLMs wholly and significantly outperform physically based models in our evaluation, likely because of the importance of a strong image prior. Results on images of real underwater scenes strongly confirm the evaluation.

Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine understanding and generation within a single architecture are a natural fit for this challenge, as their generative pathway can model the fine-grained visual structure that degradation destroys. Yet these models fail to leverage their own generative capacity on degraded inputs. We trace this disconnect to two compounding factors: existing training regimes never ask the model to invoke generation during reasoning, and the standard decode-reencode pathway does not support effective joint optimization. We present CLEAR, a framework that connects the two capabilities through three progressive steps: (1) supervised fine-tuning on a degradation-aware dataset to establish the generate-then-answer reasoning pattern; (2) a Latent Representation Bridge that replaces the decode-reencode detour with a direct, optimizable connection between generation and reasoning; (3) Interleaved GRPO, a reinforcement learning method that jointly optimizes text reasoning and visual generation under answer-correctness rewards. We construct MMD-Bench, covering three degradation severity levels across six standard multimodal benchmarks. Experiments show that CLEAR substantially improves robustness on degraded inputs while preserving clean-image performance. Our analysis further reveals that removing pixel-level reconstruction supervision leads to intermediate visual states with higher perceptual quality, suggesting that task-driven optimization and visual quality are naturally aligned.