Image denoising using block matching and discrete cosine transform with edge restoring

Enriquez, Alfredo E. Palacios; Ponomaryov, Volodymyr

doi:10.1109/conielecomp.2016.7438566

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…DCT transform-based approaches have been widely used for image denoising [27], [28]. Since noise is a high frequency component, it is better separated in the frequency domain than in the spatial domain.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

2019

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Recently, generative adversarial network-based image super resolution has been investigated, and it has been shown to lead to overwhelming improvements in subjective quality. However, it also leads to checkerboard artifacts and the unpleasing high-frequency (HF) components. In this paper, we propose a multi-discriminators-based image super resolution method that distinguishes those artifacts from various perspectives. First, the DCT perspective discriminator is proposed because the checkerboard artifacts are easily separated on the frequency domain. Second, the gradient perspective discriminator is proposed, because the unpleasing HF components can be discriminated on the gradient magnitude distribution. These proposed multi-perspective discriminators can easily identify artifacts, and they can help the generator reproduce artifact-less SR images. The experimental results show that the proposed SR-GAN with multiperspective discriminators achieves objective and subjective quality improvements in terms of PSNR, SSIM, PI and MOS, as compared to the conventional SR-GAN by reducing the aforementioned artifacts. INDEX TERMS Image super-resolution, deep learning for super resolution, SR GAN, multi-discriminators.

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Section: Related Workmentioning

confidence: 99%

“…Since noise is a high frequency component, it is better separated in the frequency domain than in the spatial domain. References [27], [28] show that the separated noise components by DCT transform can be easily removed. The gradient magnitude distribution (GMD) shows how many components each gradient of an image has.…”

Section: Related Workmentioning

confidence: 99%

Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

2019

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“…Enríquez and Ponomaryov [17] transferred the patches of the NL-Means to the frequency domain and used discrete cosine transform with a threshold to estimate true patches. Zhong et al [69] combined the NL-Means with Lee filter [36] for SAR image enhancing.…”

Section: Averaging Patch-based: Non-local Meansmentioning

confidence: 99%

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani

El-Sakka

2017

J Image Video Proc.

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Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.Keywords: Patch-based image denoising, Bilateral filter, Non-local means filtering, Probabilistic patch-based filtering, Dictionary learning filtering, K-SVD, Gaussian patch-PCA filtering, BM3D Review IntroductionThe noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based *Correspondence: elsakka@csd.uwo.ca 2 Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, N6A 5B7, London, Ontario, Canada Full list of author information is available at the end of the article image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or "blocks, " which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches.

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Non-local Means Denoising Algorithm Based on Local Binary Patterns

Kartsov

Kupriyanov

Polyakov

et al. 2020

Computer Vision in Control Systems—6

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Image denoising using block matching and discrete cosine transform with edge restoring

Cited by 7 publications

References 21 publications

Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Non-local Means Denoising Algorithm Based on Local Binary Patterns

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