A Transformer-Based Diffusion Model for All-in-One Weather-Degraded Image Restoration

Image restoration under adverse weather conditions is of great significance for the subsequent advanced computer vision tasks. However, most existing image restoration algorithms only remove single weather degradation, and few studies has been conducted on all-in-one weather-degraded image restoration. The denoising diffusion probability model is combined with Vision Transformer to propose a Transformer-based diffusion model for all-in-one weather-degraded image restoration. First, the weather-degraded image is utilized as the condition to guide the reverse sampling of diffusion model and generate corresponding clean background image. Then, the subspace transposed Transformer for noise estimation (NE-STT) is proposed, which utilizes the degraded image and the noisy state to estimate noise distribution, including the subspace transposed self-attention (STSA) mechanism and a dual grouped gated feed-forward network (DGGFFN). The STSA adopts subspace transformation coefficient to effectively capture global long-range dependencies while significantly reducing computational burden. The DGGFFN employs the dual grouped gated mechanism to enhance the nonlinear characterization ability of feed-forward network. The experimental results show that in comparison with the recently developed algorithms, such as All-in-One and TransWeather, the method proposed obtains a performance gain of 3.68 and 3.08 dB in average peak signal-to-noise ratio while 2.93% and 3.13% in average structural similarity on 5 weather-degraded datasets.