Camouflaged objects are typically assimilated into their backgrounds and
exhibit fuzzy boundaries. The complex environmental conditions and the high
intrinsic similarity between camouflaged targets and their surroundings pose
significant challenges in accurately locating and segmenting these objects in
their entirety. While existing methods have demonstrated remarkable performance
in various real-world scenarios, they still face limitations when confronted
with difficult cases, such as small targets, thin structures, and indistinct
boundaries. Drawing inspiration from human visual perception when observing
images containing camouflaged objects, we propose a three-stage model that
enables coarse-to-fine segmentation in a single iteration. Specifically, our
model employs three decoders to sequentially process subsampled features,
cropped features, and high-resolution original features. This proposed approach
not only reduces computational overhead but also mitigates interference caused
by background noise. Furthermore, considering the significance of multi-scale
information, we have designed a multi-scale feature enhancement module that
enlarges the receptive field while preserving detailed structural cues.
Additionally, a boundary enhancement module has been developed to enhance
performance by leveraging boundary information. Subsequently, a mask-guided
fusion module is proposed to generate fine-grained results by integrating
coarse prediction maps with high-resolution feature maps. Our network surpasses
state-of-the-art CNN-based counterparts without unnecessary complexities. Upon
acceptance of the paper, the source code will be made publicly available at
this https URL

本文提出了一种基于人眼视觉感知的三阶段模型，它可以在单次迭代中进行粗到细的分割，并采用了多尺度特征增强和边界增强模块。此外，还使用了掩码引导并结合了粗略预测图与高分辨率特征图来生成细致的结果。我们的网络表现优于现有 CNN 模型，降低了计算开销并减轻了背景噪声干扰。