Post Upsampling Super Resolution
The upsampling involves the usage of patch extraction. This can lead to a loss in certain features of the image that might be crucial for further processing. Hence, a post Upsampled convolution is needed to extract features.
In the post-upsampling technique, the process of upsampling is done in the end. This significantly reduces the complex computation by replacing the predefined upsampling with end-to-end learnable layers. The LR input images are given as inputs to CNN model without increasing resolution. And end-to-end learnable upsampling layers are applied at the end of the network.
Post Upsampling Super Resolution
Some popular techniques that are used in Post Sampling SR are:
- FSRCNN (Fast Super-Resolution Convolutional Neural Network)
- ESPCN (Efficient Sub-Pixel Convolutional Neural Network)
Python OpenCV – Super resolution with deep learning
Super-resolution (SR) implies the conversion of an image from a lower resolution (LR) to images with a higher resolution (HR). It makes wide use of augmentation. It forms the basis of most computer vision and image processing models. However, with the advancements in deep learning technologies, deep learning-based super resolutions have gained the utmost importance. Almost all the deep learning models would make great use of super-resolution. Since Super Resolution mainly uses augmentations of data points, it is also called hallucination of the data points.
SR plays an important role in image improvement and restoration. The SR process is carried out as follows- First, a low-resolution image is taken as the input. Next, the image is upscaled and the resolution of the images are increased to a higher resolution and given as an output.
Need for Deep learning based Super Resolution
The traditional Super Resolution Model that does not make use of Deep learning lacks fine details. They fail to remove various defects and compression facts in the systems. All of these problems can be very efficiently addressed by using a deep learning-based SR model to get an image of a higher resolution keeping all the details intact.
Some commonly used conventional SR models are
- Structured illumination microscopy (or SIM)
- Stochastic optical reconstruction microscopy (STORM)
- Photo-activated localization microscopy (PALM)
- Stimulated emission depletion (STED)