  Super Resolution is the problem of recovering a high-resolution image from a
single or multiple low-resolution images of the same scene. It is an ill-posed
problem since high frequency visual details of the scene are completely lost in
low-resolution images. To overcome this, many machine learning approaches have
been proposed aiming at training a model to recover the lost details in the new
scenes. Such approaches include the recent successful effort in utilizing deep
learning techniques to solve super resolution problem. As proven, data itself
plays a significant role in the machine learning process especially deep
learning approaches which are data hungry. Therefore, to solve the problem, the
process of gathering data and its formation could be equally as vital as the
machine learning technique used. Herein, we are proposing a new data
acquisition technique for gathering real image data set which could be used as
an input for super resolution, noise cancellation and quality enhancement
techniques. We use a beam-splitter to capture the same scene by a low
resolution camera and a high resolution camera. Since we also release the raw
images, this large-scale dataset could be used for other tasks such as ISP
generation. Unlike current small-scale dataset used for these tasks, our
proposed dataset includes 11,421 pairs of low-resolution high-resolution images
of diverse scenes. To our knowledge this is the most complete dataset for super
resolution, ISP and image quality enhancement. The benchmarking result shows
how the new dataset can be successfully used to significantly improve the
quality of real-world image super resolution.
