Multiscale super-resolution reconstruction via multibranch prediction and selection network

Convolutional neural networks have been recently shown to have the highest accuracy for single-image super-resolution reconstruction. A multibranch prediction and selection network that can gradually reconstruct robust images in multiple scales is proposed. This endeavor is achieved through a network structure with two interacting subnetworks: one is a deep cascaded, multibranch prediction network (DCMBPN) and another is a deep block-selection network (DBSN). In particular, in each cascade, DCMBPN predicts multiple reconstructed images progressively with its special multibranch and cascaded structure. DBSN then adaptively selects the predicted confident blocks from these reconstructed images. Our method does not require traditional interpolation methods to upsample the image as a preprocessing step. It, thus, greatly reduces the computational complexity. We use Euclidean and perception loss functions in each branch to obtain two high-quality reconstructions. In addition, for the cascade structure, our network can achieve reconstructions in different scales, such as 1.5x, 2x, 2.5x, 3x, 3.5x, and 4x. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and visual improvement. (C) 2018 SPIE and IS&T