Algorithm of Shape Morphing Based on Bivariate Non-linear Interpolation

Chen, Baoguo; Chen, Jinlin

doi:10.1145/3424978.3424991

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach strikes a balance between noise removal and detail signal preservation in images, potentially compensating for the loss of detail at low noise levels and residual impulse noise at high noise levels during BM3D denoising. Chen J et al addressed the issue of BM3D's inefficiency in removing severe noise contamination by introducing a local block matching algorithm based on edge search strategies and residual filtering, leveraging the high similarity of edges in remote sensing images 13 . Yahya and others replaced the traditional hard thresholding in the BM3D method with adaptive filtering techniques 14 .…”

Section: Introductionmentioning

confidence: 99%

Research on the Improved BM3D Denoising Algorithm for Remote Sensing Images Based on Context Aggregation Network

ZHOU,

Ren,

Shao

et al. 2024

Preprint

View full text Add to dashboard Cite

Denoising remote sensing images is important for their subsequent use and research. To meet the demands of image denoising in strong noise environments, this study proposes an improved BM3D remote sensing image denoising network (CBMNet) based on a context aggregation network. Initially, the original image is read, and the image outputted by BM3D filtering is stored. Following this, random patches extracted from the original image are fed into the network alongside the image processed through BM3D filtering to extract data, which is then input into the network. Subsequently, multi-scale CAN layers are established to calculate the l_2 loss function between the standard output of the BM3D filter and the network response post-processing the input image using the CAN network. Ultimately, the CBMNet network is trained to approximate the BM3D filtering operator. The experimental results indicate that, both in terms of subjective visual assessment and objective evaluation metrics, the proposed method outperforms the classical BM3D algorithm as well as the Wiener, mean, and Gaussian filtering denoising methods in removing strong Gaussian noise from remote sensing images. Additionally, the proposed method better preserves image edge details and texture information, resulting in clearer image outputs. This has significant reference value and practical utility for subsequent application research in remote sensing imagery. These results bear substantial reference significance and practical utility for subsequent applications in remote sensing imagery research.

show abstract

Section: Introductionmentioning

confidence: 99%