Gated Fusion Network for Joint Image Deblurring and Super-Resolution
This addresses the challenge of enhancing image quality in vision applications where motion blur is present, representing an incremental improvement in joint deblurring and super-resolution.
The paper tackles the problem of performing super-resolution on blurry images by proposing a deep gated fusion network that decomposes feature extraction into dual branches, resulting in sharper high-resolution images with high computational efficiency.
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.