SelfHVD: Self-Supervised Handheld Video Deblurring for Mobile Phones

Shooting video with a handheld mobile phone, the most common photographic device, often results in blurry frames due to shaking hands and other instability factors. Although previous video deblurring methods have achieved impressive progress, they still struggle to perform satisfactorily on real-world handheld video due to the blur domain gap between training and testing data. To address the issue, we propose a self-supervised method for handheld video deblurring, which is driven by sharp clues in the video. First, to train the deblurring model, we extract the sharp clues from the video and take them as misalignment labels of neighboring blurry frames. Second, to improve the model's ability, we propose a novel Self-Enhanced Video Deblurring (SEVD) method to create higher-quality paired video data. Third, we propose a Self-Constrained Spatial Consistency Maintenance (SCSCM) method to regularize the model, preventing position shifts between the output and input frames. Moreover, we construct a synthetic and a real-world handheld video dataset for handheld video deblurring. Extensive experiments on these two and other common real-world datasets demonstrate that our method significantly outperforms existing self-supervised ones. The code and datasets are publicly available at https://github.com/cshonglei/SelfHVD.

Depth-agnostic Single Image Dehazing

Single image dehazing is a challenging ill-posed problem. Existing datasets for training deep learning-based methods can be generated by hand-crafted or synthetic schemes. However, the former often suffers from small scales, while the latter forces models to learn scene depth instead of haze distribution, decreasing their dehazing ability. To overcome the problem, we propose a simple yet novel synthetic method to decouple the relationship between haze density and scene depth, by which a depth-agnostic dataset (DA-HAZE) is generated. Meanwhile, a Global Shuffle Strategy (GSS) is proposed for generating differently scaled datasets, thereby enhancing the generalization ability of the model. Extensive experiments indicate that models trained on DA-HAZE achieve significant improvements on real-world benchmarks, with less discrepancy between SOTS and DA-SOTS (the test set of DA-HAZE). Additionally, Depth-agnostic dehazing is a more complicated task because of the lack of depth prior. Therefore, an efficient architecture with stronger feature modeling ability and fewer computational costs is necessary. We revisit the U-Net-based architectures for dehazing, in which dedicatedly designed blocks are incorporated. However, the performances of blocks are constrained by limited feature fusion methods. To this end, we propose a Convolutional Skip Connection (CSC) module, allowing vanilla feature fusion methods to achieve promising results with minimal costs. Extensive experimental results demonstrate that current state-of-the-art methods. equipped with CSC can achieve better performance and reasonable computational expense, whether the haze distribution is relevant to the scene depth.

Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline's speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved Spatial Transformer Networks(STN). Meanwhile, a Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency.