|
|
|
|
|
Carmelo Scribano, Danilo Pezzi, Giorgia Franchini and Marco Prato
With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away...
ver más
|
|
|
|
|
|
|
|
|
Teresa Kwamboka Abuya, Richard Maina Rimiru and George Onyango Okeyo
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details? preservation. During the transmission of ...
ver más
|
|
|
|
|
|
|
|
|
Shengqin Bian, Xinyu He, Zhengguang Xu and Lixin Zhang
Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of ...
ver más
|
|
|
|
|
|
|
|
|
Elay Dahan and Israel Cohen
In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transdu...
ver más
|
|
|
|
|
|
|
|
|
Congyu Jiao, Fanjie Meng, Tingxuan Li and Ying Cao
Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challen...
ver más
|
|
|
|