4 research outputs found
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
Constructing high-quality character image datasets is challenging because
real-world images are often affected by image degradation. There are
limitations when applying current image restoration methods to such real-world
character images, since (i) the categories of noise in character images are
different from those in general images; (ii) real-world character images
usually contain more complex image degradation, e.g., mixed noise at different
noise levels. To address these problems, we propose a real-world character
restoration network (RCRN) to effectively restore degraded character images,
where character skeleton information and scale-ensemble feature extraction are
utilized to obtain better restoration performance. The proposed method consists
of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet
aims to preserve the structural consistency of the character and normalize
complex noise. Then, CiRNet reconstructs clean images from degraded character
images and their skeletons. Due to the lack of benchmarks for real-world
character image restoration, we constructed a dataset containing 1,606
character images with real-world degradation to evaluate the validity of the
proposed method. The experimental results demonstrate that RCRN outperforms
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images,
leading to unsatisfactory character recognition results. Existing methods have
dedicated efforts to restoring degraded character images. However, the
denoising results obtained by these methods do not appear to improve character
recognition performance. This is mainly because current methods only focus on
pixel-level information and ignore critical features of a character, such as
its glyph, resulting in character-glyph damage during the denoising process. In
this paper, we introduce a novel generic framework based on glyph fusion and
attention mechanisms, i.e., CharFormer, for precisely recovering character
images without changing their inherent glyphs. Unlike existing frameworks,
CharFormer introduces a parallel target task for capturing additional
information and injecting it into the image denoising backbone, which will
maintain the consistency of character glyphs during character image denoising.
Moreover, we utilize attention-based networks for global-local feature
interaction, which will help to deal with blind denoising and enhance denoising
performance. We compare CharFormer with state-of-the-art methods on multiple
datasets. The experimental results show the superiority of CharFormer
quantitatively and qualitatively.Comment: Accepted by ACM MM 202
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments