With “RAISR: Rapid and Accurate Image Super-Resolution”, We (Google Research) introduce a technique that incorporates machine learning in order to produce high-quality versions of low-resolution images. RAISR produces results that are comparable to or better than the currently available super-resolution methods, and does so roughly 10 to 100 times faster, allowing it to be run on a typical mobile device in real-time. Furthermore, our technique is able to avoid recreating the aliasing artifacts that may exist in the lower resolution image.
Upsampling, the process of producing an image of larger size with significantly more pixels and higher image quality from a low quality image, has been around for quite a while. Well-known approaches to upsampling are linear methods which fill in new pixel values using simple, and fixed, combinations of the nearby existing pixel values. These methods are fast because they are fixed linear filters (a constant convolution kernel applied uniformly across the image). But what makes these upsampling methods fast, also makes them ineffective in bringing out vivid details in the higher resolution results.
You can see examples and read more about the technique on the Google Research Blog
To go further read this paper (PDF) by Yaniv Romano, John Isidoro, and Peyman Milanfar
Abstract
Given an image, we wish to produce an image of larger size with significantly
more pixels and higher image quality. This is generally known as the Single
Image Super-Resolution (SISR) problem. The idea is that with sufficient
training data (corresponding pairs of low and high resolution images) we can
learn set of filters (i.e. a mapping) that when applied to given image that is
not in the training set, will produce a higher resolution version of it, where
the learning is preferably low complexity. In our proposed approach, the
run-time is more than one to two orders of magnitude faster than the best
competing methods currently available, while producing results comparable or
better than state-of-the-art.
A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a pre-processing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.
A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a pre-processing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.
See also the lastest Google information about this topic
