Change detection (CD) in remote-sensing images is one of the most crucial topics in the computer vision community. Most recent CD pipelines focus on introducing attention mechanism to enhance the discriminative ability of network, but their crude model architectures lead to inaccurate predictions and irregular boundaries. In this letter, we present a boundary-aware Siamese network (BASNet) for accurate remote-sensing CD. Based on the encoder–decoder architecture, we first propose a novel multiscale paired fusion module (MPFM) to effectively fuse the same-level feature pairs from the Siamese encoding stream. In addition, we design a location guidance module (LGM) to accurately locate the changed regions. Based on the observation that hierarchical features show different level information, we propose a multilevel feature aggregation module (MFAM) to merge the bottom-up features. Finally, we introduce a hybrid loss that fuses structural similarity (SSIM) loss and binary cross entropy (BCE) loss to focus on the structural integrity and boundary quality of changed regions. Experimental results on two public datasets demonstrate that our proposed method significantly improves the performance and outperforms the other state-of-the-art methods.

Old photos are an important carrier to preserve the past. Usually, the degradation of old photos is rather diverse and complex. Therefore, the existing methods to solve conventional restoration tasks are difficult to generalize. To solve this problem, we propose a novel method based on generative adversarial network. Our method utilizes the class-attributes of old photos to complete restoration in latent space. Specifically, we divide the process of restoring old photos into two stages, one is global defect restoration stage and the other is local detail restoration stage. In global defect restoration stage, we extract the latent representations of four classes of high-level attributes that are smoothness, clarity, connectivity and completeness. We use latent class-attribute information to restore global defects in latent space and we obtain conditional control vector through a condition network to guide the subsequent local detail restoration stage. In local detail restoration stage, we propose a dynamic condition-guided restoration module that selects the most suitable combination of features to further restore local details through a dynamic network. In addition, we propose a dual discriminator to pay more attention to style and defect restoration. We ignore the complex degradation of old photos to directly restore advanced class-attributes. Therefore, our method has better generalization performance. Experiments show that our method is superior to other existing methods of image restoration in terms of visual quality and numerical metrics.