Super-Resolution with Embedded Denoising via Image Frequency Separation and Convolutional Neural Network in a Prototyped Transcranial Ultrasound Brain Imaging Scanner

Baradarani, Aryaz; Shapoori, Kiyanoosh; Farhangfar, Saghar; Sadler, Jeff; Malyarenko, Eugene; Gelovani, Juri G.; Maev, Roman Gr.

doi:10.1109/ius51837.2023.10306624

2023 IEEE International Ultrasonics Symposium (IUS) 2023

DOI: 10.1109/ius51837.2023.10306624

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Super-Resolution with Embedded Denoising via Image Frequency Separation and Convolutional Neural Network in a Prototyped Transcranial Ultrasound Brain Imaging Scanner

Aryaz Baradarani,

Kiyanoosh Shapoori,

Saghar Farhangfar

et al.

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2024

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Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks

Farhangfar,

Baradarani,

Asadpour

et al. 2024

IET Image Processing

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Given that super‐resolution (SR) aims to recover lost information, and low‐resolution (LR) images in real‐world conditions might be corrupted with multiple degradations, considering basic bicubic down‐sampling as the sole degradation significantly limits the performance of most existing SR models. This paper presents a model for simultaneous super‐resolution and blind additive white Gaussian noise (AWGN) denoising with two components (netdeg and netSR) that is based on a generative adversarial network (GAN) to achieve detailed results. netdeg, featuring residual and innovative cost‐effective ghost residual blocks with a frequency separation module for obtaining long‐range information, blindly restores a clean version of the LR image. netSR leverages slim ghost full‐frequency residual blocks to process low‐frequency (LF) and high‐frequency (HF) information via static large convolutions and pixel‐wise highlighted input‐adaptive dynamic convolutions, respectively. To address the susceptibility of dynamic layers to noise and preserve feature diversity while reducing model’s costs, static and dynamic layer features are combined and highlighted. Diverse and non‐redundant features are then processed using ghost‐style blocks. The proposed model achieves comparable SR results in bicubic down‐sampling scenarios, outperform existing SR methods in the complex task of concurrent SR and AWGN denoising, and demonstrate robustness in handling images corrupted with varying levels of AWGN.

show abstract

Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks

Farhangfar,

Baradarani,

Asadpour

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

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Super-Resolution with Embedded Denoising via Image Frequency Separation and Convolutional Neural Network in a Prototyped Transcranial Ultrasound Brain Imaging Scanner

Cited by 1 publication

References 10 publications

Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks

Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks

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