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ESPCN: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network ( implementation).

EDSR: Enhanced Deep Residual Networks for Single Image Super-Resolution ( implementation).

If you would like to read more about these models, I’ve included their names, implementations, and paper links below: A review of the model architectures, how they work, and the training process of each respective model is outside the scope of this guide (as we’re focusing on implementation only). We’ll be utilizing four pre-trained super resolution models in this tutorial. By applying novel deep learning architectures, we’re able to generate high resolution images without these artifacts:įigure 3: Example of a super resolution architecture compatible with the OpenCV library ( image source). The answer is yes - and it’s not magic either. Is there a better way to increase the resolution of the image without degrading the quality?

Cv2 image resize Patch#

However, take a second to note how pixelated, blurry, and just unreadable the image patch is after applying bicubic interpolation. On the bottom we have the output of applying bicubic interpolation, the standard interpolation method used for increasing the size of input images (and what we commonly use in cv2.resize when needing to increase the spatial dimensions of an input image). The area highlighted in the red rectangle is the area we wish to extract and increase the resolution of (i.e., resize to a larger width and height without degrading the quality of the image patch). Applying bicubic interpolation to this region yields poor results.

We wish to increase the resolution of the area in the red rectangle. … however, if you increase the spatial dimensions too much, then the output image appears pixelated, has artifacts, and in general, just looks “aesthetically unpleasing” to the human eye.įor example, let’s consider the following figure:įigure 1: On the top we have our original input image. These functions “work” in the sense that an input image is presented, the image is resized, and then the resized image is returned to the calling function … all use classic interpolation techniques and algorithms (ex., nearest neighbor interpolation, linear interpolation, bicubic interpolation) to increase the image resolution. That’s because Photoshop, GIMP, Image Magick, OpenCV (via the cv2.resize function), etc. More simply, take an input image and increase the width and height of the image with minimal (and ideally zero) degradation in quality.Īnyone who has ever opened a small image in Photoshop or GIMP and then tried to resize it knows that the output image ends up looking pixelated. Super resolution encompases a set of algorithms and techniques used to enhance, increase, and upsample the resolution of an input image. We’ll wrap up this tutorial with a discussion of our results.

Cv2 image resize how to#

How specialized deep learning architectures can help us achieve super resolution in real-timeįrom there, I’ll show you how to implement OpenCV super resolution with both:.

Why we can’t use simple nearest neighbor, linear, or bicubic interpolation to substantially increase the resolution of images.

In the first part of this tutorial, we will discuss:

Cv2 image resize code#

Looking for the source code to this post? Jump Right To The Downloads Section OpenCV Super Resolution with Deep Learning

To learn how to use OpenCV for deep learning-based super resolution, just keep reading. The function requires a bit of explanation to use, so I decided to author a tutorial on it that way everyone can learn how to use OpenCV’s super resolution functionality.īy the end of this tutorial, you’ll be able to perform super resolution with OpenCV in both images and real-time video streams! However, OpenCV’s super resolution functionality is actually “hidden” in a submodule named in dnn_superres in an obscure function called DnnSuperResImpl_create.

Pre-trained (meaning you don’t have to train them yourself on a dataset).

You’re in luck, Hisham - there are super resolution deep neural networks that are both: If you have any suggestions, that would be a big help.” It was super helpful, thank you.Īre there any pre-trained super resolution models compatible with OpenCV’s dnn module? “Hi Adrian, I read your Deep Learning for Computer Vision with Python book and went through your super resolution implementation with Keras and TensorFlow.