XYDeblur: Divide and Conquer for Single Image Deblurring
Seo-Won Ji, Jeongmin Lee, Seungwook Kim, Jun-Pyo Hong, Seung-Jin Baek, Seung‐Won Jung, Sung-Jea Ko
Abstract
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images. Having long been proven to be effective in image restoration tasks, a single lane of encoder-decoder architecture overlooks the characteristic of deblurring, where a blurry image is generated from complicated blur kernels caused by tangled motions. Toward an effective network architecture for single image deblurring, we present complemental sub-solution learning with a one-encoder-two-decoder architecture. Observing that multiple decoders successfully learn to decompose encoded feature information into directional components, we further improve both the network efficiency and the deblurring performance by rotating and sharing kernels exploited in the decoders, which prevents the decoders from separating unnecessary components such as color shift. As a result, our proposed network shows superior results compared to U-Net while preserving the network parameters, and using the proposed network as the base network can improve the performance of existing state-of-the-art deblurring networks.