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The use of ultrasound (US) as an imaging technique is essential for the diagnosis of atherosclerotic cardiovascular disease (ASCVD), which depends on US images of the carotid artery. However, US images are plagued by a specific type of noise called Speckle noise, which lowers image quality dramatically. As an attempt to improve US image quality, th...
Citations
... Pix2pix [21] was introduced as a generalpurpose framework for image-to-image translation tasks, using conditional generative adversarial networks (cGANs). Several works extended pix2pix to image denoising tasks [24, 43,52]. Additionally, the widely-used content-aware image restoration (CARE) network [58] incorporates a U-Net architecture [45] to denoise low-resolution fluorescence data. ...
Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability.
... They have been used in image denoising in several studies. Authors in [34] use a Pix2Pix GAN to denoise ultrasound images. Speckle noise using Rayleigh distribution was added to the training images to generate image pairs of original and noisy images. ...
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the quality of the training data that constitutes crowd images. In harsh weather such as fog, dust, and low light conditions, the inference performance may severely degrade on the noisy and blur images. In this paper, we propose the use of Pix2Pix generative adversarial network (GAN) to first denoise the crowd images prior to passing them to the counting model. A Pix2Pix network is trained using synthetic noisy images generated from original crowd images and then the pretrained generator is then used in the inference engine to estimate the crowd density in unseen, noisy crowd images. The performance is tested on JHU-Crowd dataset to validate the significance of the proposed method particularly when high reliability and accuracy are required.
