Single-molecule localization microscopy techniques, like stochastic optical
reconstruction microscopy (STORM), visualize biological specimens by
stochastically exciting sparse blinking emitters. The raw images suffer from
unwanted background fluorescence, which must be removed to achieve
super-resolution. We introduce a sparsity-based background removal method by
adapting a neural network (SLNet) from a different microscopy domain. The SLNet
computes a low-rank representation of the images, and then, by subtracting it
from the raw images, the sparse component is computed, representing the frames
without the background. We compared our approach with widely used background
removal methods, such as the median background removal or the rolling ball
algorithm, on two commonly used STORM datasets, one glial cell, and one
microtubule dataset. The SLNet delivers STORM frames with less background,
leading to higher emitters' localization precision and higher-resolution
reconstructed images than commonly used methods. Notably, the SLNet is
lightweight and easily trainable (<5 min). Since it is trained in an
unsupervised manner, no prior information is required and can be applied to any
STORM dataset. We uploaded a pre-trained SLNet to the Bioimage model zoo,
easily accessible through ImageJ. Our results show that our sparse
decomposition method could be an essential and efficient STORM pre-processing
tool.

一种基于稀疏性的背景去除方法通过使用来自不同显微镜领域的神经网络（SLNet）计算图像的低秩表示，并从原始图像中减去它，从而计算出不带背景的稀疏成分，该方法在比较常用的背景去除方法上实现了更少背景的 STORM 帧，提高了荧光发射体的定位精度和高分辨率图像的重构。

基于稀疏性的 STORM 超分辨率图像背景去除

Sparsity-based background removal for STORM super-resolution images

Fashion understanding is a hot topic in computer vision, with many
applications having great business value in the market. Fashion understanding
remains a difficult challenge for computer vision due to the immense diversity
of garments and various scenes and backgrounds. In this work, we try removing
the background from fashion images to boost data quality and increase model
performance. Having fashion images of evident persons in fully visible
garments, we can utilize Salient Object Detection to achieve the background
removal of fashion data to our expectations. A fashion image with the
background removed is claimed as the "rembg" image, contrasting with the
original one in the fashion dataset. We conducted extensive comparative
experiments with these two types of images on multiple aspects of model
training, including model architectures, model initialization, compatibility
with other training tricks and data augmentations, and target task types. Our
experiments show that background removal can effectively work for fashion data
in simple and shallow networks that are not susceptible to overfitting. It can
improve model accuracy by up to 5% in the classification on the FashionStyle14
dataset when training models from scratch. However, background removal does not
perform well in deep neural networks due to incompatibility with other
regularization techniques like batch normalization, pre-trained initialization,
and data augmentations introducing randomness. The loss of background pixels
invalidates many existing training tricks in the model training, adding the
risk of overfitting for deep models.

通过移除时尚图像的背景以提高数据质量和模型性能，本文对时尚图像进行背景去除的研究进行了实验，结果表明背景去除能够有效地提高简单网络模型在时尚数据分类任务上的准确性，但在深度神经网络中与其他正则化技术不兼容，存在过拟合的风险。

背景去除对时尚图像分类和分割神经网络性能的影响

The Impact of Background Removal on Performance of Neural Networks for  Fashion Image Classification and Segmentation

A fully automated object reconstruction pipeline is crucial for digital
content creation. While the area of 3D reconstruction has witnessed profound
developments, the removal of background to obtain a clean object model still
relies on different forms of manual labor, such as bounding box labeling, mask
annotations, and mesh manipulations. In this paper, we propose a novel
framework named AutoRecon for the automated discovery and reconstruction of an
object from multi-view images. We demonstrate that foreground objects can be
robustly located and segmented from SfM point clouds by leveraging
self-supervised 2D vision transformer features. Then, we reconstruct decomposed
neural scene representations with dense supervision provided by the decomposed
point clouds, resulting in accurate object reconstruction and segmentation.
Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the
effectiveness and robustness of AutoRecon.

本文介绍了一种名为 AutoRecon 的新型框架，该框架利用自我监督的 2D 视觉转换器功能，通过多视图图像自动发现和重建对象，并使用分解的神经场景表示进行密集监督，从而实现了准确的对象重建和分割。