Pre – Upsampling Super Resolution
Upsampling is a technique that implies the doubling of a simple layer of the input layer. It is then followed by the convolution filtering. Generally, bicubic interpolation is used for the same.
Pre-Upsampling Super Resolution
As we can see from the example above, the lower resolution (LR) image undergoes a patch extraction. Patch extraction is the process of extracting the dense features from the image and convolve it. In the upsampling model, the convolution filters are present. They help in non-linear mapping. Furthermore, the convolved patch is reconstructed resulting in the high resolution (HR) image.
Some of the common techniques, used for Upsampling an image, are:
- SRCNN (Super Resolution Convolutional Neural Network)
- VDSR (Very Deep Super Resolution)
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)