Domain Transform Image Raindrop Removal Method by Integrating Fast Fourier Convolution
Owing to the significant impact of raindrops on the quality of images under rainy conditions and the current over-reliance on paired images in existing rain removal methods,achieving unsupervised image rain removal remains a challenging research problem.To address this issue,this study proposes a domain transform image raindrop removal method.Building a Domain Transform Network(DTN)that transforms between rainy and rain-free domains to achieve unsupervised raindrop removal.Additionally,Fast Fourier Convolution(FFC)is introduced to design the generation and discrimination networks,enabling the interactions of global and local features.Within FFC,Spectral Transformation(ST)is employed to transform between spatial and frequency domains,overcoming the limited receptive field problem of a traditional Convolutional Neural Network(CNN)and enhancing the perception of subtle raindrops.Deraining experiments conducted on two real raindrop test sets demonstrate that our method outperforms existing advanced methods in terms of quantitative results and visual effects.Compared with the original U-Net plus Markov discriminant network,the improved method improves the Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index Measure(SSIM)by 3.37 dB and 0.031 3,respectively,and can restore more image texture details while removing raindrops.
万方数据
维普
