The aim of blind super-resolution (SR) in computer vision is to improve the
resolution of an image without prior knowledge of the degradation process that
caused the image to be low-resolution. The State of the Art (SOTA) model
Real-ESRGAN has advanced perceptual loss and produced visually compelling
outcomes using more complex degradation models to simulate real-world
degradations. However, there is still room to improve the super-resolved
quality of Real-ESRGAN by implementing recent techniques. This research paper
introduces StarSRGAN, a novel GAN model designed for blind super-resolution
tasks that utilize 5 various architectures. Our model provides new SOTA
performance with roughly 10% better on the MANIQA and AHIQ measures, as
demonstrated by experimental comparisons with Real-ESRGAN. In addition, as a
compact version, StarSRGAN Lite provides approximately 7.5 times faster
reconstruction speed (real-time upsampling from 540p to 4K) but can still keep
nearly 90% of image quality, thereby facilitating the development of a
real-time SR experience for future research. Our codes are released at
this https URL

StarSRGAN 是一种用于盲超分辨率任务的 GAN 模型，通过实验比较与 Real-ESRGAN，提供了新的最先进性能，MANIQA 和 AHIQ 度量方面大约提高了 10%；另外，StarSRGAN Lite 是一种紧凑版模型，提供了大约 7.5 倍更快的重建速度，但仍能保持近 90% 的图像质量，从而有助于未来研究中开发实时超分辨率体验。

StarSRGAN: 提高实际世界中的盲目超分辨率

StarSRGAN: Improving Real-World Blind Super-Resolution

In this study, we evaluate the performance of multiple state-of-the-art SR
GAN (Super Resolution Generative Adversarial Network) models, ESRGAN,
Real-ESRGAN and EDSR, on a benchmark dataset of real-world images which undergo
degradation using a pipeline. Our results show that some models seem to
significantly increase the resolution of the input images while preserving
their visual quality, this is assessed using Tesseract OCR engine. We observe
that EDSR-BASE model from huggingface outperforms the remaining candidate
models in terms of both quantitative metrics and subjective visual quality
assessments with least compute overhead. Specifically, EDSR generates images
with higher peak signal-to-noise ratio (PSNR) and structural similarity index
(SSIM) values and are seen to return high quality OCR results with Tesseract
OCR engine. These findings suggest that EDSR is a robust and effective approach
for single-image super-resolution and may be particularly well-suited for
applications where high-quality visual fidelity is critical and optimized
compute.

评估多个最新的 SR GAN 模型（超分辨率生成对抗网络），包括 ESRGAN、Real-ESRGAN 和 EDSR，在经过流程处理的现实世界图像基准数据集上的性能，结果表明某些模型能够显著提高图像分辨率同时保留其视觉质量，使用 Tesserect OCR 引擎进行评估，其中 EDSR 模型在定量指标和主观视觉质量评估上优于其他模型，具有最少的计算开销，并且生成的图像具有更高的峰值信噪比（PSNR）和结构相似性指数（SSIM）值，通过 Tesseract OCR 引擎返回高质量的 OCR 结果，这些发现表明 EDSR 是一种稳健有效的单图像超分辨率方法，特别适用于对高质量视觉保真度至关重要且经过优化计算的应用。

SR-GAN 模型的比较分析

A comparative analysis of SR-GAN models

Though many attempts have been made in blind super-resolution to restore
low-resolution images with unknown and complex degradations, they are still far
from addressing general real-world degraded images. In this work, we extend the
powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN),
which is trained with pure synthetic data. Specifically, a high-order
degradation modeling process is introduced to better simulate complex
real-world degradations. We also consider the common ringing and overshoot
artifacts in the synthesis process. In addition, we employ a U-Net
discriminator with spectral normalization to increase discriminator capability
and stabilize the training dynamics. Extensive comparisons have shown its
superior visual performance than prior works on various real datasets. We also
provide efficient implementations to synthesize training pairs on the fly.

本研究将 ESRGAN 应用于实际的纹理恢复应用中，在处理复杂的真实世界退化图像时，引入高阶退化建模过程，并考虑到常见的环绕和过冲伪影，采用具有谱归一化的 U-Net 鉴别器来增强鉴别器的能力和稳定训练动力学， 并提供了实时生成训练样本的高效实现。