Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
This work addresses the gap between simulated and real-world degradations in SISR, which is crucial for practical applications like photography and mobile imaging, though it is incremental as it builds on existing SISR methods with a new dataset and model.
The authors tackled the problem of single image super-resolution (SISR) in real-world scenarios by creating a new dataset (RealSR) with paired low- and high-resolution images captured using cameras, and they developed a Laplacian pyramid kernel prediction network (LP-KPN) to handle non-uniform degradations, resulting in models that deliver better visual quality with sharper edges and finer textures compared to those trained on simulated data.
Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i.e., bicubic downsampling) to their high-resolution (HR) counterparts. However, the degradations in real-world LR images are far more complicated. As a consequence, the SISR models trained on simulated data become less effective when applied to practical scenarios. In this paper, we build a real-world super-resolution (RealSR) dataset where paired LR-HR images on the same scene are captured by adjusting the focal length of a digital camera. An image registration algorithm is developed to progressively align the image pairs at different resolutions. Considering that the degradation kernels are naturally non-uniform in our dataset, we present a Laplacian pyramid based kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels to recover the HR image. Our extensive experiments demonstrate that SISR models trained on our RealSR dataset deliver better visual quality with sharper edges and finer textures on real-world scenes than those trained on simulated datasets. Though our RealSR dataset is built by using only two cameras (Canon 5D3 and Nikon D810), the trained model generalizes well to other camera devices such as Sony a7II and mobile phones.