Curvelet thresholding with multiscale NLM filtering for color image denoising

Panigrahi, Susant Kumar; Gupta, Shipra; Sahu, Prasanna Kumar

doi:10.1109/tencon.2017.8228230

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…5 is designed specifically to handle the real-world noisy image with a single tuning parameter. By adopting several changes compared to the initial algorithm as in [35], we developed a fast, efficient and flexible, denoising method using a standalone model that can handle both spatially variant and invariant noise when the noise standard-deviation is known or unknown for real-time applications. Moreover, we purely focused on multi-channel (RGB) images contaminated with natural sensor noise especially due to low-light conditions.…”

Section: Methodsmentioning

confidence: 99%

“…To reduce ringing artifacts due to hard thresholding. Out of two solutions listed in [35], and inspired from the seminal work of Dabov et al [32], we considered luminance /color-difference based de-correlated space for image denoising, as shown in Fig. 5.…”

Section: Methodsmentioning

confidence: 99%

“…Literature suggests that with proper tuning parameters of C GIF , improves the denoising performance up to 1.0dB [41]. Moreover, compared to its earlier versions proposed in [35], for computational tractability and ease of usage a single input parameter, σ est (estimated noise standard deviation) is used to tune all other parameters in Eq. 10.…”

Section: B Problem Formulationmentioning

confidence: 99%

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Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Panigrahi,

Tripathy,

Bhowmick

et al. 2024

IEEE Access

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Na ïve simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-of-the-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied "The Curious Case of the Finest Scale" and constructed "Deep Curvelet-Net": an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes denoised FC under the supervision of encoder's multiscale spatial attention map. The "Deep Curvelet-Net" links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: B Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Panigrahi,

Tripathy,

Bhowmick

et al. 2024

IEEE Access

Self Cite

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Primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising

Tang

Yang

2019

J. Electron. Imag.

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Real-world image denoising via efficient diffusion model with controllable noise generation

Yang,

Wang,

Liang

et al. 2024

J. Electron. Imag.

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Real-world image denoising is a critical task in image processing, aiming to restore clean images from their noisy counterparts captured in natural environments. While diffusion models have demonstrated remarkable success in image generation, surpassing traditional generative models, their application to image denoising has been limited due to challenges in controlling noise generation effectively. We present a general denoising method inspired by diffusion models. Specifically, our approach employs a diffusion process with linear interpolation, enabling control of noise generation. By interpolating the intermediate noisy image between the original clean image and the corresponding real-world noisy one, our model is able to achieve controllable noise generation. Moreover, we introduce two sampling algorithms for this diffusion model: a straightforward procedure aligned with the diffusion process and an enhanced version that addresses the shortcomings of the former. Experimental results demonstrate that our proposed method, utilizing simple convolutional neural networks such as UNet, achieves denoising performance comparable to that of the transformer architecture.

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Curvelet thresholding with multiscale NLM filtering for color image denoising

Cited by 3 publications

References 27 publications

Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising

Real-world image denoising via efficient diffusion model with controllable noise generation

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