Introduction:
Multi-scale Progressive Fusion Network (MSFPN) is a neural network representation designed for single image deraining, which helps remove the rain streaks from images. The network aims to leverage the related information available on different scales of rain streaks to improve the derain performance.
Deraining using MSFPN:
With MSFPN, we use the Gaussian kernel to down-sample the original image to generate the Gaussian pyramid rain images. This image is then fed into the Multi-scale Progressive Fusion Network (MSFPN) which does the following:
- The Coarse Fusion Module (CFM) captures the global information of the multi-scale rain images using recurrent calculation, thus enabling the network to cooperatively represent the target rain streak using similar counterparts from a global feature space.
- The representation of the high-resolution pyramid layer is guided by previous outputs as well as all low-resolution pyramid layers.
- A Fine Fusion Module (FFM) further integrates these associated pieces of information from different scales. The channel attention mechanism helps to learn the scale-specific knowledge effectively while reducing the feature redundancy. Multiple FFM can be linked to form a progressive multi-scale fusion.
- Finally, the Reconstruction Module (RM) is appended to aggregate the coarse and fine rain information extracted respectively from CFM and FFM for learning the residual rain image, which is the approximation of the real rain streak distribution.
Features of MSFPN:
MSFPN has the following features that contribute to its effectiveness:
- It takes into account the relationship of rain streaks across scales, which helps remove a substantial amount of rain streaks in a single image.
- The network is designed to capture the information from different scales and fuse it to improve the derain performance.
- The network is trained to learn the residual rain image effectively, which helps approximate the real rain distribution and improve the image quality.
- The use of the channel attention mechanism helps to learn the scale-specific knowledge effectively, while also reducing the feature redundancy.
Applications of MSFPN:
MSFPN has many practical applications in areas including transportation, self-driving cars, and computer vision. For example, in autonomous vehicles, line-of-sight visibility can be impaired due to rainy weather. MSFPN can help improve the images taken by the cameras in the vehicle, thereby improving safety. Furthermore, in computer vision, MSFPN can be used to improve the accuracy of image recognition systems that may have difficulty identifying objects in an image due to rain streaks.
Conclusion:
Multi-scale Progressive Fusion Network (MSFPN) is an innovative neural network designed to improve the derain performance for single images. This network has practical applications in areas such as autonomous vehicles, transportation, and computer vision. MSFPN is designed to capture multi-scale information and fuse it to improve the image's quality, making it a very effective tool, especially in rainy weather conditions.
