Phase-Preserved Curvelet Thresholding for Image Denoising

Panigrahi, Susant Kumar; Gupta, Shipra

doi:10.1007/s40031-022-00780-0

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Owing to energy compact and linearity properties (due to tight frames), the multiresolution curvelet transform can represent any square integrable function and also obeys Parsevals' theorem. As a result the noise may remain additive in the transformed domain and NLM filter can be applied on the curvelet coefficients [2]. With a single parameter based image denoising framework, we highlight the main contribution of our work:…”

Section: A Significant Contributionmentioning

confidence: 99%

“…The increasing demand of high (spatial) resolution images, with constant die size of CCD sensors, imaging systems invariably add unwanted noise components while acquir-ing images. Higher pixel counts under limited sensor size damages the signal integrity at each pixel by receiving less photons (lights) and resulting less charges and lower signal to noise ratio [1], [2]. As modifying imaging systems is almost impractical, thus developing densoing algorithms is a key indispensable step in many image processing and computer vision tasks [3].…”

Section: Introductionmentioning

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: A Significant Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold

Ren,

Wang,

Chen

et al. 2024

Front. Earth Sci.

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In low-resolution remote sensing images under complex lighting conditions, there is a similarity in spectral characteristics between non-landslide areas and landslide bodies, which increases the probability of misjudgment in the identification process of shallow landslide bodies. In order to further improve the accuracy of landslide identification, a shallow landslide remote sensing identification method based on an improved Otsu algorithm and multi-feature threshold is proposed for the temporary treatment project of the Yangjunba disaster site in Leshan City. Using Retinex theory, remote sensing images are enhanced with local linear models and guided filtering; then, multi-feature scales and sliding window calculations of opening and closing transformations identify potential landslide areas, which are finally segmented using the Otsu algorithm. Through experimental verification, the method proposed in this article can clearly segment the target object and background after binary segmentation of remote sensing images. The recognition rate of shallow landslide bodies is not less than 95%, indicating that the method proposed in this article is relatively accurate in identifying shallow landslide bodies in the research area and has good application effects.

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Phase-Preserved Curvelet Thresholding for Image Denoising

Cited by 2 publications

References 32 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

Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold

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