![]() ![]() ![]() In this case, round truth is the original image which was downscaled to create the low-resolution image. The objective is to improve the quality of the LR image to approach the quality of the target, known as the ground truth. The target image or ground truth, which was downscaled to create the lower resolution input.The input image is upscaled and improved by our Deep Learning Super Resolution model.The input image is upscaled by Lanczos' interpolation (one of the best standard methods).The input image is upscaled by Bi-linear interpretation (the most common used method).The input image is upscaled by Nearest-Neighbour interpolation.Click on the image below to get a closer look at each result, as well as the original image before it was downscaled. Then, the various methods can be applied. The group of images below demonstrates these different options.įirst, the lower resolution input image to be be upscaled: Upscaling can be achieved using different techniques, such as the aformentioned Nearest-Neighbor, Linear and Lanczos resampling methods. Recent years have witnessed dramatic improvements in the design and training of CNN models used by Super-Resolution. The key to DLSR succsss is the recent rapid development of deep convolutional neural networks (CNNs). The problem is thus ill-posed, and the quality of the SR result is limited.ĭLSR solves this problem by learning image prior information from HR and/or LR example images, thereby improving the quality of the LR to HR transformation. Unfortunately we usually do not know the degradation function beforehand. If we know the degradation function in advance, we can apply its inverse to the LR image to recover the HR image. The HR and LR images are related via the equation: LR = degradation(HR).īy applying the degradation function, we obtain the LR image from the HR image. SR increases high frequency components and removes compression artifacts. Super-resolution (SR) is a technique for constructing a high-resolution (HR) image from a collection of observed low-resolution (LR) images. We now have a whole new class of techniques for state-of-the-art upscaling, called Deep Learning Super Resolution (DLSR).Īn image's resolution may be reduced due to lower spatial resolution (for example to reduce bandwidth) or due to image quality degradation such as blurring. ![]() Also, traditional algorithm-based upscaling methods lack fine detail and cannot remove defects and compression artifacts.Īll of this is changing thanks to the Deep Learning revolution. However, no fundamentally new methods have been introduced in over 20 years. ![]() The upscaling community has provided us with many fundamental advances in video and image upscaling, from classic methods such as Nearest-Neighbor, Linear and Lanczos resampling. Quality is also degraded by routine image upscaling, which is required to match the very high pixel density of newer mobile devices. Due to bandwidth limitations, most video sources are compressed, resulting in image artifacts, noise, and blur. Video delivery quality depends critically on available network bandwidth. Streaming now accounts for over 60% of internet traffic and is expected to quadruple over the next five years. Internet streaming has experienced tremendous growth in the past few years, and continues to advance at a rapid pace. Here's an overview of Super Resolution, its purpose for image and video upscaling, and how our model came about. At Collabora we have addressed this issue by introducing an accurate and light-weight deep network for video super-resolution, running on a completely open source software stack using Panfrost, the free and open-source graphics driver for Mali GPUs. Despite their great upscaling performance, deep learning backed Super-Resolution methods cannot be easily applied to real-world applications due to their heavy computational requirements. ![]()
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