Perceptual-based super-resolution reconstruction using image-specific degradation estimation

Aarizou, Kawther; Loukil, Abdelhamid

doi:10.1117/1.jei.30.3.033007

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Chudasama and Upla 22 presented E-ProSRGAN with a computationally efficient enhanced progressive approach for better visually plausible solutions. Aarizou and Loukil 23 made use of the power of deep generative models to capture latent representations of patches across two scales and train a downscaling CNN to learn how to downscale the image by matching these latent distributions. Lei et al 24 .…”

Section: Related Workmentioning

confidence: 99%

Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

et al. 2023

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In recent years, deep neural networks have exhibited promising performance in the single-image super-resolution (SISR) task by designing complex network structures from the low-resolution (LR) images to the high-resolution (HR) images and learning the mapping function. However, the SISR task is a widely known ill-posed problem. Since it may map one LR image input to multiple HR images, there are always undesired structural distortions in the recovered SR images. We creatively propose a SISR network with dynamic multi-task learning and multi-level mutual feature fusion (DMMFSR), alleviating structural distortion problems while maintaining more edge structure. Specifically, we utilize a DML block (DMLB) to train low-frequency and high-frequency image information extraction networks so the two networks can mutually promote each other. In addition, we also use a multi-level mutual feature fusion block to fuse image and edge features at different levels. Extensive experiments demonstrate the effectiveness of our proposed DMMFSR. Meanwhile, our model achieves comparable peak signal-to-noise ratio and structural similarity scores on multiple SR benchmarks compared with state-of-the-art SR methods.

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Section: Related Workmentioning

confidence: 99%

Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

et al. 2023

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Super-Resolution Virtual Scene Rendering Technology Based on Generalized Huber-MRF Image Modeling

Mao,

Rao,

Chen

et al. 2024

Int J Comput Intell Syst

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The traditional rendering technology creates virtual scenes with insufficient fidelity, which are quite different from real scenes. To address this issue, a super-resolution virtual scene rendering technology based on generalized Huber-MRF image modeling has been studied. This study preprocesses the original image through three steps: graying, filtering, and enhancement. The generalized Huber-MRF is employed for super-resolution image restoration to enhance image clarity. Corner features are extracted from the super-resolution image, and the Delaunay triangular grid method is used to construct the image's 3D model. Texture and lighting conditions of the virtual scene are then set through texture mapping, shadow rendering, and other technologies to achieve realistic scene effects. The results indicate that, when applied, the research technology yields a relatively small chamfer distance in virtual scene modeling, suggesting that the design method preserves the details and shape information of the original image, reducing the difference between the virtual scene and the real scene and increasing the fidelity of the virtual scene. Furthermore, this method achieves maximum PSNR and SSIM values of 17.54 and 0.978, respectively, with an image preprocessing time of only 1.21 s and a CPU utilization rate of only 35.5%. This method demonstrates excellent performance across multiple aspects.

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Perceptual-based super-resolution reconstruction using image-specific degradation estimation

Cited by 2 publications

References 29 publications

Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

Super-Resolution Virtual Scene Rendering Technology Based on Generalized Huber-MRF Image Modeling

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