Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation

Witwit, Wasnaa; Zhao, Yifan; Jenkins, Karl W.; Zhao, Yitian

doi:10.1117/1.jei.26.2.023014

Cited by 16 publications

(6 citation statements)

References 38 publications

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“…Since the definition of the tonality range of Eq. (2) is relatively simple, the concept of related total variation is introduced to improve (Xu et al, 2012), and the correlation total variation is defined as…”

Section: Bilateral Texture Filtering Of Wavelet Low-frequency Sub-bandmentioning

confidence: 99%

Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

Jia

Dongye

2020

Nonlin. Processes Geophys.

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Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. Firstly, the image is wavelet decomposed to obtain a low-frequency structural component and a series of high-frequency texture detail components. Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail. Finally, the processed high- and low-frequency components reconstructed by wavelets can obtain a seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.

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Section: Bilateral Texture Filtering Of Wavelet Low-frequency Sub-bandmentioning

confidence: 99%

Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

Jia

Dongye

2020

Nonlin. Processes Geophys.

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“…Satellite imagery (SI) has been utilized for different practical applications namely surveillance, education, agriculture, navigation, disaster mitigation, regional planning, and many more [1]. SI can produce different types of highresolution images that can be either grayscale or color [2]. The perceived quality of satellite images hinges on the electromagnetic energy that is reflected from the objects at the surface of the earth [3].…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

Al-Ameen¹

2020

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The quality of satellite images is frequently degraded by the low-contrast effect. Therefore, different research works have been developed to deal with this undesirable effect. Still, no conclusive verdicts have been reached and the problem remains open for research. Hence, an ameliorated balance contrast enhancement technique using a parabolic function (ABCETP) is proposed in this article to improve the quality in terms of brightness, contrast, and colors. The original BCETP works by determining a parabolic function with three coefficients. However, the proposed ABCETP works by computing a modified parabolic function with one modified and two unmodified coefficients, as well as, it utilizes two additional methods to produce adequate-quality results. The proposed ABCETP is examined with numerous real contrast-distorted images, compared against different contrast enhancement methods and the results are assessed with two specialized quality appraisal metrics. Empirical results obtained from conducting various comparisons and experiments revealed the favorability of ABCETP, wherein it outputted images with better-perceived quality and outperformed the comparative methods in several aspects.

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“…It has an urgent requirement for SR in many fields, such as computer vision, medical image processing and remote sensing image processing 1 . SR methods can be mainly divided into three categories: interpolation-based methods 3 , reconstruction-based methods and learning-based methods. Learning-based methods can be divided into shallow learning SR methods and deep learning SR methods.…”

Section: Introductionmentioning

confidence: 99%

Super-resolved image perceptual quality improvement via multifeature discriminators

Zhu¹,

Cheng²,

Peng³

et al. 2020

J. Electron. Imag.

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Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. In this paper, we design a new GAN-based SR framework GAN-IMC which includes generator, image discriminator, morphological component discriminator and color discriminator. The combination of multiple feature discriminators improves the accuracy of image discrimination. Adversarial training between the generator and multi-feature discriminators forces SR images to converge with HR images in terms of data and features distribution. Moreover, in some cases, feature enhancement of salient regions is also worth considering. GAN-IMC is further optimized by weighted content loss (GAN-IMCW), which effectively restores and enhances salient regions in SR images. The effectiveness and robustness of our method are confirmed by extensive experiments on public datasets. Compared with state-of-the-art methods, the proposed method not only achieves competitive Perceptual Index (PI) and Natural Image Quality Evaluator (NIQE) values but also obtains pleasant visual perception in image edge, texture, color and salient regions. ).2 reconstruction quality compared with state-of-the-art methods at that time due to the close correlation between pixel loss function and Power Signal-to-Noise Ratio (PSNR). This research is a milestone for SR. Subsequently, considerable researches 10,13,14,15,16 that minimized pixel loss function to train CNNs had been conducted and PSNR values had been dramatically improved. However, the studies 17-19 pointed out that SR results with good visual quality reflected by PSNR values were inconsistent with or even contrary to the subjective evaluation of human observers. Blurry edges and over-smooth textures were shown in SR results while having a high PSNR value. Both Perceptual Index (PI) 25 and Natural Image Quality Evaluator (NIQE) 28 are brought up to evaluate SR results in terms of perceptual quality.In order to improve SR images visual quality, researchers have introduced different loss functions to optimize SR networks. In 2017, Ledge et al. 17 presented a generative adversarial network (GAN) 8 composed of a generator and an image discriminator for SR. The generator is used to generate SR results. The discriminator is used to determine SR images and HR images. Adversarial learning between generator and discriminator encourages some or several types of data of SR images to be similar to that of HR images. Park et al. 22 proposed a feature discriminator that distinguishes SR image from HR image by feature maps to produce high-frequency details.Considering that morphological component and color are highly correlated with image visual quality, a natural idea is to introduce morphological component discriminator and color discriminator to identify images. In this pa...

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Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation

Cited by 16 publications

References 38 publications

Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

Satellite Image Enhancement Using an Ameliorated Balance Contrast Enhancement Technique

Super-resolved image perceptual quality improvement via multifeature discriminators

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