43 research outputs found
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
Rainy weather significantly deteriorates the visibility of scene objects,
particularly when images are captured through outdoor camera lenses or
windshields. Through careful observation of numerous rainy photos, we have
found that the images are generally affected by various rainwater artifacts
such as raindrops, rain streaks, and rainy haze, which impact the image quality
from both near and far distances, resulting in a complex and intertwined
process of image degradation. However, current deraining techniques are limited
in their ability to address only one or two types of rainwater, which poses a
challenge in removing the mixture of rain (MOR). In this study, we propose an
effective image deraining paradigm for Mixture of rain REmoval, called
DEMore-Net, which takes full account of the MOR effect. Going beyond the
existing deraining wisdom, DEMore-Net is a joint learning paradigm that
integrates depth estimation and MOR removal tasks to achieve superior rain
removal. The depth information can offer additional meaningful guidance
information based on distance, thus better helping DEMore-Net remove different
types of rainwater. Moreover, this study explores normalization approaches in
image deraining tasks and introduces a new Hybrid Normalization Block (HNB) to
enhance the deraining performance of DEMore-Net. Extensive experiments
conducted on synthetic datasets and real-world MOR photos fully validate the
superiority of the proposed DEMore-Net. Code is available at
https://github.com/yz-wang/DEMore-Net.Comment: 11 pages, 7 figures, 5 table
MTRNet++: One-stage Mask-based Scene Text Eraser
A precise, controllable, interpretable and easily trainable text removal
approach is necessary for both user-specific and large-scale text removal
applications. To achieve this, we propose a one-stage mask-based text
inpainting network, MTRNet++. It has a novel architecture that includes
mask-refine, coarse-inpainting and fine-inpainting branches, and attention
blocks. With this architecture, MTRNet++ can remove text either with or without
an external mask. It achieves state-of-the-art results on both the Oxford and
SCUT datasets without using external ground-truth masks. The results of
ablation studies demonstrate that the proposed multi-branch architecture with
attention blocks is effective and essential. It also demonstrates
controllability and interpretability.Comment: This paper is under CVIU review (after major revision