A denoising approach via wavelet domain diffusion and image domain diffusion

Zhang, Xiaobo

doi:10.1007/s11042-016-3778-3

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the tensor diffusion proposed by Weickert et al in [3] is reviewed firstly. And then, the coherence enhancement anisotropic diffusion in [3] and the contrast enhancement using modified edge enhancement anisotropic diffusion proposed by Zhang in [8] are described, respectively. Finally, to clearly present the proposed method further, the structure of proposed algorithm is demonstrated in detail.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In [8], a modified edge enhancement anisotropic diffusion (MEEAN) was proposed. That is because that the contrast ratio of the initial denoised image is reduced compared with the original observed noisy image.…”

Section: The Modified Edge Enhancement Anisotropic Diffusionmentioning

confidence: 99%

“…In [7], by developing multi-scale decimation-free directional filter bank, the improved coherence enhancement diffusion is used to process the fingerprint image. In [8], Zhang produced an image enhancement scheme based on anisotropic tensor diffusion. This method is used as the post-processing scheme in the process of image denoising.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two Stage Fingerprint Image Enhancement Algorithm by Anisotropic Diffusion

Zhang¹,

Ma²,

Wang³

et al. 2019

dtcse

View full text Add to dashboard Cite

A new fingerprint enhancement algorithm is presented. The technique consists of two anisotropic diffusion schemes. One is the coherence-enhancing diffusion. Another is the modified edge-enhancing diffusion. At first, the degraded fingerprint image is processed by the coherence-enhancing diffusion to obtain the initial processed image. And then, the modified edge-enhancing diffusion operates on the initial processed image to obtain the final enhancement image. The tests show the proposed new method gets the better performance obviously compared to the original coherence-enhancing diffusion-based method.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Modified Edge Enhancement Anisotropic Diffusionmentioning

confidence: 99%

See 1 more Smart Citation

Two Stage Fingerprint Image Enhancement Algorithm by Anisotropic Diffusion

Zhang¹,

Ma²,

Wang³

et al. 2019

dtcse

View full text Add to dashboard Cite

show abstract

“…Transform domain filtering involves taking a noisy image and filtering it in the transform domain to obtain a denoised image. This process includes methods such as wavelet transform domain [18,19] and Fourier transform domain [20]. The Block-Matching and 3D filtering (BM3D) [21] method utilizes self-similar patches to attain superior outcomes with 2 of 22 respect to both image fidelity and visual quality.…”

Section: Introductionmentioning

confidence: 99%

RCA-GAN: An Improved Image Denoising Algorithm Based on Generative Adversarial Networks

Wang,

Luo,

et al. 2023

Electronics

View full text Add to dashboard Cite

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.

show abstract