Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
This addresses a critical limitation in blind super-resolution for real-world images, where blur varies spatially, offering improved accuracy for applications like photography and vision systems.
The paper tackles the problem of spatially variant blur kernels in blind image super-resolution, which existing methods assume are invariant, by proposing a mutual affine network (MANet) that achieves state-of-the-art performance in both synthetic and real image experiments.
Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for real images whose blur kernels are usually spatially variant due to factors such as object motion and out-of-focus. Hence, existing blind SR methods would inevitably give rise to poor performance in real applications. To address this issue, this paper proposes a mutual affine network (MANet) for spatially variant kernel estimation. Specifically, MANet has two distinctive features. First, it has a moderate receptive field so as to keep the locality of degradation. Second, it involves a new mutual affine convolution (MAConv) layer that enhances feature expressiveness without increasing receptive field, model size and computation burden. This is made possible through exploiting channel interdependence, which applies each channel split with an affine transformation module whose input are the rest channel splits. Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.