Relaxed Asymmetric Deep Hashing Learning: Point-to-Angle Matching

Focus Affinity Perception and Super-Resolution Embedding for Multifocus Image Fusion

Despite the fact that there is a remarkable achievement on multifocus image fusion, most of the existing methods only generate a low-resolution image if the given source images suffer from low resolution. Obviously, a naive strategy is to independently conduct image fusion and image super-resolution. However, this two-step approach would inevitably introduce and enlarge artifacts in the final result if the result from the first step meets artifacts. To address this problem, in this article, we propose a novel method to simultaneously achieve image fusion and super-resolution in one framework, avoiding step-by-step processing of fusion and super-resolution. Since a small receptive field can discriminate the focusing characteristics of pixels in detailed regions, while a large receptive field is more robust to pixels in smooth regions, a subnetwork is first proposed to compute the affinity of features under different types of receptive fields, efficiently increasing the discriminability of focused pixels. Simultaneously, in order to prevent from distortion, a gradient embedding-based super-resolution subnetwork is also proposed, in which the features from the shallow layer, the deep layer, and the gradient map are jointly taken into account, allowing us to get an upsampled image with high resolution. Compared with the existing methods, which implemented fusion and super-resolution independently, our proposed method directly achieves these two tasks in a parallel way, avoiding artifacts caused by the inferior output of image fusion or super-resolution. Experiments conducted on the real-world dataset substantiate the superiority of our proposed method compared with state of the arts.

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPNs) further address class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a fine-grained subtype-aware alignment to improve the performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype while exhibiting disparate characteristics because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multiview congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtype-aware UDA, compared with state-of-the-art UDA methods.

From Global to Local: Multi-Patch and Multi-Scale Contrastive Similarity Learning for Unsupervised Defocus Blur Detection

Defocus blur detection (DBD), which aims to detect out-of-focus or in-focus pixels from a single image, has been widely applied to many vision tasks. To remove the limitation on the abundant pixel-level manual annotations, unsupervised DBD has attracted much attention in recent years. In this paper, a novel deep network named Multi-patch and Multi-scale Contrastive Similarity (M2CS) learning is proposed for unsupervised DBD. Specifically, the predicted DBD mask from a generator is first exploited to re-generate two composite images by transporting the estimated clear and unclear areas from the source image to realistic full-clear and full-blurred images, respectively. To encourage these two composite images to be completely in-focus or out-of-focus, a global similarity discriminator is exploited to measure the similarity of each pair in a contrastive way, through which each two positive samples (two clear images or two blurred images) are enforced to be close while each two negative samples (a clear image and a blurred image) are inversely far. Since the global similarity discriminator only focuses on the blur-level of a whole image and there do exist some fail-detected pixels which only cover a small part of areas, a set of local similarity discriminators are further designed to measure the similarity of image patches in multiple scales. Thanks to this joint global and local strategy, as well as the contrastive similarity learning, the two composite images are more efficiently moved to be all-clear or all-blurred. Experimental results on real-world datasets substantiate the superiority of our proposed method both in quantification and visualization. The source code is released at: https://github.com/jerysaw/M2CS.

Layer-Output Guided Complementary Attention Learning for Image Defocus Blur Detection

Defocus blur detection (DBD), which has been widely applied to various fields, aims to detect the out-of-focus or in-focus pixels from a single image. Despite the fact that the deep learning based methods applied to DBD have outperformed the hand-crafted feature based methods, the performance cannot still meet our requirement. In this paper, a novel network is established for DBD. Unlike existing methods which only learn the projection from the in-focus part to the ground-truth, both in-focus and out-of-focus pixels, which are completely and symmetrically complementary, are taken into account. Specifically, two symmetric branches are designed to jointly estimate the probability of focus and defocus pixels, respectively. Due to their complementary constraint, each layer in a branch is affected by an attention obtained from another branch, effectively learning the detailed information which may be ignored in one branch. The feature maps from these two branches are then passed through a unique fusion block to simultaneously get the two-channel output measured by a complementary loss. Additionally, instead of estimating only one binary map from a specific layer, each layer is encouraged to estimate the ground truth to guide the binary map estimation in its linked shallower layer followed by a top-to-bottom combination strategy, gradually exploiting the global and local information. Experimental results on released datasets demonstrate that our proposed method remarkably outperforms state-of-the-art algorithms.