Breaking through scattering: The H-Net CNN model for image retrieval

In scattering media, traditional optical imaging techniques often find it significantly challenging to accurately reconstruct images owing to rapid light scattering. Thus, to address this problem, we propose a convolutional neural network architecture called H-Net, which is specifically designed to recover structural information from images distorted by scattering media.

Our approach involves the use of dilated convolutions to capture local and global features of the distorted images, allowing for the effective reconstruction of the underlying structures. First, we developed a diffuse image dataset by projecting handwritten numbers through diffusers with different thicknesses, capturing the resulting distorted images. Second, we generated a synthetic speckle images dataset, composed of simulated speckle patterns. These datasets were designed to train the model to recover structures within scattering media. To evaluate the model's performance, we calculated the Structural Similarity Measure Index between the model's predictions and the original images on unseen data.

This proposed architecture achieves reconstructions with an average structural similarity index measure of 0.8 while maintaining low computational costs.

The results of this study indicate that H-Net offers an alternative to more complex and computationally expensive models, providing efficient and reliable image reconstruction in scattering media.