Reflection removal is essential for applications in photography, object detection, and augmented reality. Single-image reflection removal (SIRR) offers greater flexibility and applicability than multi-image methods, making it ideal for real-time scenarios. However, strong reflections obscure large portions of the transmission layer, limiting the performance of existing methods. We propose a novel user-assisted approach for SIRR, where users annotate occluded objects by selecting their categories and locations. This provides critical semantic information to guide accurate transmission layer recovery. Additionally, we design a hybrid CNN-Transformer network that leverages local feature extraction and global context modeling to address strong reflection challenges. Experiments on the strong reflection datasets demonstrate the effectiveness of our method, achieving significant improvements in transmission layer recovery and outperforming existing advanced methods across multiple metrics.

When photographing through glass, reflections are often observed, which negatively impact the quality of the captured images or videos. In this article, we summarize and rethink depth guided reflection removal methods and, inspired by the human binocular vision system, investigate how to utilize depth for effective binocular video reflection removal. We propose an end-to-end learning-based reflection removal method that learns the transmission depth and designs a unified structure to achieve depth guided, cross-view, and cross-frame feature enhancement in a cascaded manner. Within the unified structure, different gating controllers are custom-designed to emphasize the direction of feature interaction. A dataset containing synthetic and real binocular mixture video dataset is built for network training and testing. Experimental results on both synthetic and real data from the proposed dataset demonstrate that the proposed method achieves superior performance in binocular video reflection removal.

Single image reflection removal (SIRR) aims at eliminating unwanted interference caused by the reflection of transparent or smooth surfaces and obtaining an estimation of a clear transmission layer. Existing data-driven methods typically rely on decomposing the observed image into transmission and reflection layers, which neglects the physical generation principles of an image with reflections, thus leading to unsatisfactory results, especially in strong reflection regions. To address this issue, in this work, we analyze the imaging process of reflection image from the physical perspective and derive a conclusion that the physical quantity: illuminance of the reflection layer determines the reflection intensity. Then a two-stage reflection intensity-guided network (RINet) is proposed for reflection removal and transmission recovery. The key lies in the first stage are the parallel modules that generate the reflection intensity map and transmission layer. In the second stage, besides utilizing such intensity map as the guidance, we additionally calculate the gradient field as the other prior to facilitate the final reflection removal. Specifically, we design a dual-flow joint learning module (JLM) comprised of a transmission recovery branch and a gradient optimization branch that jointly optimizes image structures and details by exploiting the interactions between transmission and gradient features. In particular, guided by the reflection intensity map, the transmission recovery branch can dynamically focus on removing reflections. Equipped with the two-stage framework, our RINet constitutes a divide-and-conquer process to achieve effective transmission recovery and reflection removal. Experimental results on public datasets demonstrate the superiority of the proposed method over recent state-of-the-art methods.

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which up-scales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use down-sampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.

Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an extra branch to extract high-frequency detail information from RGB image to guide the low-resolution depth map reconstruction. However, because there are still some differences between the two modalities, direct information transmission in the feature dimension or edge map dimension cannot achieve satisfactory result, and may even trigger texture copying in areas where the structures of the RGB-D pair are inconsistent. Inspired by the multi-task learning, we propose a joint learning network of depth map super-resolution (DSR) and monocular depth estimation (MDE) without introducing additional supervision labels. For the interaction of two subnetworks, we adopt a differentiated guidance strategy and design two bridges correspondingly. One is the high-frequency attention bridge (HABdg) designed for the feature encoding process, which learns the high-frequency information of the MDE task to guide the DSR task. The other is the content guidance bridge (CGBdg) designed for the depth map reconstruction process, which provides the content guidance learned from DSR task for MDE task. The entire network architecture is highly portable and can provide a paradigm for associating the DSR and MDE tasks. Extensive experiments on benchmark datasets demonstrate that our method achieves competitive performance. Our code and models are available at https://rmcong.github.io/proj_BridgeNet.html.

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.