Wavelet-Transform-Based Neural Network for Tidal Flat Remote Sensing Image Deblurring

In response to the challenge of image degradation caused by strong sea breezes during drone surveillance over tidal flats, conventional methodologies have predominantly employed iterative upsampling and downsampling techniques to augment the receptive field of the network. However, this approach is prone to the loss of critical texture data within the tidal flat imagery throughout the sampling process. To mitigate these issues and enhance the recovery of sharp imagery from blurred inputs, we introduce a novel deep learning architecture based on traditional physical models. Our network structure mainly consists of two parts: Initially, applying wavelet transform to the input images, the extracted high-frequency components are refined using a combination of Bayesian adaptive thresholding and hard thresholding. This process not only ensures high fidelity of the high-frequency information but also contributes to generating tidal flat images with clearer texture details. Subsequently, given the relatively large scope of images captured by drones, there is an increased emphasis on the importance of contextual information during the feature extraction process. To this end, we have applied dilated convolution modules with varying dilation rates to the low-frequency components. This design enables the network to capture image features at different scales, enhancing the model's understanding of the tidal flat scene context and improving its feature expression capability. This allows the model to more accurately identify and extract blurred areas, thereby improving the deblurring effect. Additionally, we have incorporated a loss function based on the wavelet transform. This function guides the model to recover clear details from blurred images by minimizing the differences between the high-frequency and low-frequency components of the original clear image and those of the deblurred image. In the quantitative assessment of the real tidal flat image dataset, we observed that the algorithm has a parameter volume of 7.8M and has achieved significant performance improvement: the peak signal-to-noise ratio (PSNR) reached 33.11, and the structural similarity index reached 0.7909. The enhancement of these metrics indicates that the algorithm excels in the recovery of image texture details while maintaining a compact parameter count. The optimized parameter configuration not only improves the algorithm's operational efficiency but also simplifies the deployment and training process of the model, making it more suitable for tidal flat scenarios. © 2025 IEEE.