With the growing amount of astronomical data, there is an increasing need for
automated data processing pipelines, which can extract scientific information
from observation data without human interventions. A critical aspect of these
pipelines is the image quality evaluation and masking algorithm, which
evaluates image qualities based on various factors such as cloud coverage, sky
brightness, scattering light from the optical system, point spread function
size and shape, and read-out noise. Occasionally, the algorithm requires
masking of areas severely affected by noise. However, the algorithm often
necessitates significant human interventions, reducing data processing
efficiency. In this study, we present a deep learning based image quality
evaluation algorithm that uses an autoencoder to learn features of high quality
astronomical images. The trained autoencoder enables automatic evaluation of
image quality and masking of noise affected areas. We have evaluated the
performance of our algorithm using two test cases: images with point spread
functions of varying full width half magnitude, and images with complex
backgrounds. In the first scenario, our algorithm could effectively identify
variations of the point spread functions, which can provide valuable reference
information for photometry. In the second scenario, our method could
successfully mask regions affected by complex regions, which could
significantly increase the photometry accuracy. Our algorithm can be employed
to automatically evaluate image quality obtained by different sky surveying
projects, further increasing the speed and robustness of data processing
pipelines.

本研究提出了一种基于深度学习的图像质量评估算法，利用自编码器学习高质量天文图像的特征，从而实现自动评估图像质量和噪声区域的遮盖，提高数据处理效率。该算法经过两种情景的测试，证明能够有效识别点扩散函数和复杂背景所影响的区域，可用于不同的天文巡天项目中的图像质量评估，进一步提高数据处理的速度和稳定性。

基于预训练深度神经网络的图像质量评估与掩模算法

An Image Quality Evaluation and Masking Algorithm Based On Pre-trained  Deep Neural Networks

It is well-known that there is no universal metric for image quality
evaluation. In this case, distortion-specific metrics can be more reliable. The
artifact imposed by image compression can be considered as a combination of
various distortions. Depending on the image context, this combination can be
different. As a result, Generalization can be regarded as the major challenge
in compressed image quality assessment. In this approach, stacking is employed
to provide a reliable method. Both semantic and low-level information are
employed in the presented IQA to predict the human visual system. Moreover, the
results of the Full-Reference (FR) and No-Reference (NR) models are aggregated
to improve the proposed Full-Reference method for compressed image quality
evaluation. The accuracy of the quality benchmark of the clic2024 perceptual
image challenge was achieved 79.6\%, which illustrates the effectiveness of the
proposed fusion-based approach.

在这项研究中，研究者采用了一种基于融合的方法对压缩图像质量进行评估，通过利用畸变特定的度量方法并结合全参考和非参考模型的结果，可提高对压缩图像质量的评估准确性，达到了 79.6％的质量基准准确率，证明了该融合方法的有效性。

使用层叠技术进行压缩图像质量评估

Compressed image quality assessment using stacking

Convolutional neural networks have recently been used for multi-focus image
fusion. However, due to the lack of labeled data for supervised training of
such networks, existing methods have resorted to adding Gaussian blur in
focused images to simulate defocus and generate synthetic training data with
ground-truth for supervised learning. Moreover, they classify pixels as focused
or defocused and leverage the results to construct the fusion weight maps which
then necessitates a series of post-processing steps. In this paper, we present
unsupervised end-to-end learning for directly predicting the fully focused
output image from multi-focus input image pairs. The proposed approach uses a
novel CNN architecture trained to perform fusion without the need for ground
truth fused images and exploits the image structural similarity (SSIM) to
calculate the loss; a metric that is widely accepted for fused image quality
evaluation. Consequently, we are able to utilize {\em real} benchmark datasets,
instead of simulated ones, to train our network. The model is a feed-forward,
fully convolutional neural network that can process images of variable sizes
during test time. Extensive evaluations on benchmark datasets show that our
method outperforms existing state-of-the-art in terms of visual quality and
objective evaluations.

本文介绍了一种基于卷积神经网络的无监督端到端学习方法，可以直接从多焦点输入图像对中预测完全聚焦的输出图像，并利用图像结构相似性 (SSIM) 计算损失。实验结果表明，该方法在视觉质量和客观度量方面优于现有的最先进技术。