Deep Image Prior for Super Resolution of Noisy Image

Han, Sujy; Lee, Tae Bok; Heo, Yong Seok

doi:10.3390/electronics10162014

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…2). In contrast, these super-resolution methods generally smooth the noise with a blurring effect [VCSR21], [MNV + 22] or apply a denoising filter before the super-resolution [HLH21].…”

Section: Introductionmentioning

confidence: 99%

Learning-based low-rank denoising

Cammarasana

Patanè

2022

SIViP

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The denoising of 2D images through low-rank methods is a relevant topic in digital image processing. This paper proposes a novel method that trains a learning network to predict the optimal thresholds of the singular value decomposition involved in the low-rank denoising of 2D images. To improve the denoising results, we apply the block-matching algorithm and classify each 3D block according to four parameters, which increase the specificity of the network for the prediction of the thresholds. Our method outperforms state-of-the-art methods for image denoising; furthermore, it is general with respect to the type of noise and provides an upper bound to the accuracy of the denoising of 2D images through the Singular Value Decomposition.

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“…2). In contrast, these super-resolution methods generally smooth the noise with a blurring effect [VCSR21], [MNV + 22] or apply a denoising filter before the super-resolution [HLH21].…”

Section: Introductionmentioning

confidence: 99%

Learning-based low-rank denoising

Cammarasana

Patanè

2022

SIViP

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“…Li et al [ 50 ] presented a low-light image enhancement method based on a deep symmetric encoder–decoder convolutional network. Han et al [ 51 ] proposed a DIP based on a noise-robust super resolution method. Ai and Kwon [ 52 ] used attention U-Net for extreme low-light image enhancement.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

Gao

Zhang

Luo

2022

Sensors

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Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.

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“…A more recent method introduced as S2SUCNN [25] combines these two classes of techniques, training a CNN in an unsupervised manner by introducing an MTF-based degradation layer as their final processing step. Their method is based on the idea of deep image prior [26], which provides a solving mechanism for inverse problems, such as denoising, inpainting, and super-resolution [27], [28], using neural network structures to model a reliable inverse mapping through standard gradient-based optimization, under a difference minimization objective.…”

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confidence: 99%

A CNN-Based Sentinel-2 Image Super-Resolution Method Using Multiobjective Training

Vasilescu

Datcu

Faur

2023

IEEE Trans. Geosci. Remote Sensing

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Deep learning methods have become ubiquitous tools in many Earth observation applications, delivering stateof-the-art results while proving to generalize for a variety of scenarios. One such domain concerns the Sentinel-2 (S2) satellite mission, which provides multispectral images in the form of 13 spectral bands, captured at three different spatial resolutions: 10, 20, and 60 m. This research aims to provide a super-resolution mechanism based on fully convolutional neural networks (CNNs) for upsampling the low-resolution (LR) spectral bands of S2 up to 10-m spatial resolution. Our approach is centered on attaining good performance with respect to two main properties: consistency and synthesis. While the synthesis evaluation, also known as Wald's protocol, has spoken for the performance of almost all previously introduced methods, the consistency property has been overlooked as a viable evaluation procedure. Recently introduced techniques make use of sensor's modulation transfer function (MTF) to learn an approximate inverse mapping from LR to high-resolution images, which is on a direct path for achieving a good consistency value. To this end, we propose a multiobjective loss for training our architectures, including an MTF-based mechanism, a direct input-output mapping using synthetically degraded data, along with direct similarity measures between high-frequency details from already available 10-m bands, and super-resolved images. Experiments indicate that our method is able to achieve a good tradeoff between consistency and synthesis properties, along with competitive visual quality results.

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Deep Image Prior for Super Resolution of Noisy Image

Cited by 6 publications

References 30 publications

Learning-based low-rank denoising

Learning-based low-rank denoising

Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior

A CNN-Based Sentinel-2 Image Super-Resolution Method Using Multiobjective Training

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