E-SEVSR—Edge Guided Stereo Endoscopic Video Super-Resolution

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The adoption of Stereo Imaging technology within endoscopic procedures represents a transformative advancement in medical imaging, providing surgeons with depth perception and detailed views of internal anatomy for enhanced diagnostic accuracy and surgical precision. However, the practical application of stereo imaging in endoscopy faces challenges, including the generation of low-resolution and blurred images, which can hinder the effectiveness of medical diagnoses and interventions. Our research introduces an endoscopic image SR model in response to these specific. This model features an innovative feature extraction module and an advanced cross-view feature interaction module tailored for the intricacies of endoscopic imagery. Initially trained on the SCARED dataset, our model was rigorously tested across four additional publicly available endoscopic image datasets at scales 2, 4, and 8, demonstrating unparalleled performance improvements in endoscopic SR. Our results are compelling. They show that our model not only substantially enhances the quality of endoscopic images but also consistently surpasses other existing methods like E-SEVSR [6], DCCSRNet [10], and CCSBESR [5] in all tested datasets, in quantitative measures such as PSNR and SSIM, and in qualitative evaluations. The successful application of our SR model in endoscopic imaging has the potential to revolutionize medical diagnostics and surgery, significantly increasing the precision and effectiveness of endoscopic procedures. The code will be released on GitHub and can be accessed at https://github.com/cu-vtrg-lab/ Saliency-Aware-Deep-Learning-Approach-for-Enhanced-Endoscopic-Image-SR.

Section: Methodsmentioning

confidence: 99%

Saliency-Aware Deep Learning Approach for Enhanced Endoscopic Image Super-Resolution

Hayat,

Aramvith

2024

Self Cite

Super-Resolution Reconstruction of Cell Images Based on Generative Adversarial Networks

Pan,

Du,

Guo

2024

In this study, we introduce Light-ESRGAN, a novel cellular image super-resolution reconstruction model utilizing Generative Adversarial Networks (GANs). High-resolution cellular images are pivotal in pathological research; however, how to caputer critical features such as cell edges during microscopic imaging presents challenges due to hardware limitations and environmental factors. These factors frequently introduce noise and interference. Rapid advancements in deep learning have significantly enhanced the field of image super-resolution reconstruction, demonstrating substantial potential in cellular image processing. Our Light-ESRGAN employs a random degradation modeling process to leverage the GAN architecture for more accurate simulations of real-world degradation. We have also incorporated the Convolutional Block Attention Module (CBAM) into the residual blocks of the generator, thereby enhancing its ability to reconstruct image edges and textures through the fusion of channel and spatial attentions. For the discriminator, a lightweight U-Net structure is adopted, which not only reduces the model's parameter size but also improves its discriminative capacity. Compared to Real-ESRGAN and A-ESRGAN, Light-ESRGAN has reduced model parameters by 34.7% and 66.4%, respectively. It has demonstrated improved performance on publicly available cellular images, increasing the average Peak Signal-to-Noise Ratio (PSNR) by 1.174 dB and 1.992 dB, the Structural Similarity Index (SSIM) by an average of 1.5% and 9.1%, and reducing the Normalized Root Mean Square Error (NRMSE) by an average of 0.005 and 0.009.

Semantic-Aware Guided Low-Light Image Super-Resolution

Ren,

Cao,

Tan

et al. 2024

The single image super-resolution based on deep learning has achieved extraordinary performance. However, due to inevitable environmental or technological limitations, some images not only have low resolution but also low brightness. The existing super-resolution methods for restoring images through low-light input may encounter issues such as low brightness and many missing details. In this paper, we propose a semantic-aware guided low-light image super-resolution method. Initially, we present a semantic perception guided super-resolution framework that utilizes the rich semantic prior knowledge of the semantic network module. Through the semantic-aware guidance module, reference semantic features and target image features are fused in a quantitative attention manner, guiding low-light image features to maintain semantic consistency during the reconstruction process. Second, we design a self-calibrated light adjustment module to constrain the convergence consistency of each illumination estimation block by selfcalibrated block, improving the stability and robustness of output brightness enhancement features. Third, we design a lightweight super resolution module based on spatial and channel reconstruction convolution, which uses the attention module to further enhances the super-resolution reconstruction capability. Our proposed model surpasses methods such as RDN, RCAN, and NLSN in both qualitative and quantitative analysis of low-light image super-resolution reconstruction. The experiment proves the efficiency and effectiveness of our method.