As virtual and augmented reality applications gain popularity, omnidirectional image (ODI) super-resolution has become increasingly important. Unlike 2D plain images that are formed on a plane, ODIs are projected onto spherical surfaces. Applying established image super-resolution methods to ODIs, therefore, requires performing equirectangular projection (ERP) to map the ODIs onto a plane. ODI super-resolution needs to take into account geometric distortion resulting from ERP. However, without considering such geometric distortion of ERP images, previous methods only utilize a limited range of pixels and may easily miss self-similar textures for reconstruction. In this paper, we introduce a novel Geometric Distortion Guided Transformer for Omnidirectional image Super-Resolution (GDGT-OSR). Specifically, a distortion modulated rectangle-window selfattention mechanism, integrated with deformable self-attention, is proposed to better perceive the distortion and thus involve more self-similar textures. Distortion modulation is achieved through a newly devised distortion guidance generator that produces guidance for the rectangular windows by exploiting the variability of distortion across latitudes. Furthermore, we propose a dynamic feature aggregation scheme to adaptively fuse the features from different self-attention modules. We present extensive experimental results on public datasets and show that the new GDGT-OSR outperforms methods in existing literature.

Anchor-free object detectors have recently received increasing research attention in the field of aerial scene object detection, due to their high flexibility and practicality. Anchor-free detectors typically depend on the feature pyramid network (FPN) to alleviate the challenge of significant variations in object scales in aerial contexts. Despite establishing a multi-scale feature pyramid, existing FPN-based methods treat each aerial object as an indivisible entity solely managed by a single-scale representation. However, they fail to take into account the distinct characteristics of various components within an instance. To this end, this letter proposes a novel anchor-free detector, namely IMPR-Det, which can integrally mix multi-scale pyramid representations for different components of an instance, thus boosting the fine-grained object representation capability. Specifically, IMPR-Det fundamentally introduces a more advanced detection head with an adaptive routing mechanism for pixel-level multi-scale feature assignment, instead of previous instance-level assignment. Experimental results demonstrate the superiority of the proposed method over its counterparts, in terms of both accuracy and efficiency, for object detection in aerial images.

How to handle an arbitrary number for input images is a fundamental problem of learning-based photometric stereo methods. Existing approaches adopt max-pooling or observation map to fuse an arbitrary number of extracted features. However, these methods discard a large amount of the features from the input images, impacting the utilization and accuracy, or ignore the constraints from the intra-image spatial domain. In this paper, we explore how to efficiently fuse features from a variable number of input images. First, we propose a bilateral extraction module, which categorizes features into positive and negative, to maximally keep the useful feature in the fusion stage. Second, we adopt a top-k pooling to both the bilateral information, which selects the k maximum response value from all features. These two modules proposed are "plug-and-play" and can be used in different fusion tasks. We further propose a hierarchical photometric stereo network, namely HPS-Net, to handle bilateral extraction and top-k pooling for multiscale features. Experiments in the widely used benchmark illustrate the improvement of our proposed framework in the conventional max-pooling method and the proposed HPS-Net outperforms existing learning-based photometric stereo methods.

Deep learning-based methods have achieved promising results in single image super-resolution (SISR). However, the performance of existing deep SISR methods is very sensitive to image degradation. In addition, these methods are deterministic and do not introduce any uncertainty to the generated images, so we have no way of knowing the reliability of these generated images. To address these two challenging issues, we propose a model-agnostic approach for existing deep SISR networks to improve their robustness under various degradations. Our proposed method follows a probabilistic framework and applies Monte Carlo dropout to existing deep SISR methods. Instead of performing point estimation, the proposed method predicts the posterior distribution of super-resolved images. Based on this, we can determine the uncertainty of the generated images. Experiment results show that the proposed method can effectively improve the robustness of existing deep SISR methods, leading to state-of-the-art performance when applied to images having different degradations. The code is available at https://github.com/YangTracy/MCD-SR.