Resolution Enhancement refers to a group of techniques that are used in image processing or super-resolution microscopy for scaling up and improving low-resolution input images. Resolution enhancement has various applications, such as security and surveillance imaging, medical imaging, image generation, and satellite and astronomical imaging. There are both single and multiple image variants of resolution enhancement methods. saiwa provides two single image resolution enhancement methods using deep learning: Residual Dense Network (RDN) and Residual in Residual Dense Network (RRDN). They both use residual learning that has also been widely adopted to ease the training process, either in image-level or feature-level. For more details of the two methods and network architecture, please refer to the white paper.
RDN networks weights are ready to use for scale-up with a factor of 2 and RRDN for scale-up with a factor of 4. For other scale-up factors, please fill-up the “Request for customization” form.
saiwa resolution enhancement service benefits from following features:
- Providing state-of-the-art deep super-resolution networks
- Exporting and archiving the results in user cloud space or locally
- Request for re-train the networks for other scaling factors by saiwa© team using “Request for customization” option
- Apply on several images
- Preview and download the resulting imagesResolution enhancement has wide range of applications including:1. Security and surveillance imaging
2. Medical imaging
3. Image reconstruction
4. Satellite and astronomical imaging