Kronecker component with robust low-rank dictionary for image denoising

Zhang, Lei; liu, cong

doi:10.1016/j.displa.2022.102194

Cited by 3 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sunder Ali et al [20] proposed a cascaded and recursive convolutional neural network (CRCNN) framework, which could cope with spatial variant noise and blur artifacts in a single denoising framework. Lei Zhang et al [21] designed a novel denoising model named Kronecker Component with Low-Rank Dictionary (KCLD), which replaced the Frobenius norm with a Nuclear norm in order to capture the low-rank property better. Phan et al [22] proposed an adaptive model that used the mean curvature of an image surface to control the strength of smoothing.…”

Section: The Related Workmentioning

confidence: 99%

Deep Signal-Dependent Denoising Noise Algorithm

Zhao

Wang

2023

Electronics

View full text Add to dashboard Cite

Although many existing noise parameter estimations of image signal-dependent noise have certain denoising effects, most methods are not ideal. There are some problems with these methods, such as poor noise suppression effects, smooth details, lack of flexible denoising ability, etc. To solve these problems, in this study, we propose a deep signal-dependent denoising noise algorithm. The algorithm combines the model method with a convolutional neural network. We use the noise level of the noise image and the noise image together as the input of the convolutional neural network to obtain a wider range of noise levels than the single noise image as the input. In the convolutional neural network, the deep features of the image are extracted by multi-layer residuals, which solves the difficult problem of training. Extensive experiments demonstrate that our noise parameter estimation has good denoising performance.

show abstract

Section: The Related Workmentioning

confidence: 99%