Image enhancement using DT-CWT based cycle spinning methodology

Kundeti, Naga Mahesh; Kalluri, Hemantha Kumar; Krishna, S. Korrapati Mohan

doi:10.1109/iccic.2013.6724213

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…x , σ 2 y ), and structure (σ xy ). PSNR and SSIM are widely used to evaluate the performance of low-light image-processing methods [22,24,26,39,40] and remote-sensing image-processing methods [20,41,42]. With the help of PSNR and SSIM, we can effectively evaluate the color retention and structural differences between enhanced images and reference images.…”

Section: Trainingmentioning

confidence: 99%

RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

Qin

Zhang

et al. 2020

Remote Sensing

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Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.

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Section: Trainingmentioning

confidence: 99%

RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

Qin

Zhang

et al. 2020

Remote Sensing

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“…To preserve high frequency image details an Adaptive antialiasing algorithm based on the wavelet Fourier transform and directionally adaptive wavelet shrinkage is used. [2], [3].It removes aliasing artifacts by shrinking transform coefficients. An effective resolution enhancement approach for images such as Satellite images as well as normal images DT-CWT and bicubic interpolation is used to generate the unknown high resolution image.…”

Section: Introductionmentioning

confidence: 99%

An effective method for satellite image enhancement

Jadhav¹,

Patil²

2015

International Conference on Computing, Communication &Amp; Automation

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In the field of image processing, Satellite imaging is one of the challenging tasks for the researchers. The different satellite sensors are available in the very low resolution to high resolution range for data collection.In this paper, a satellite image enhancement algorithm based on interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the low resolution input image is proposed. This method uses a DWT and high frequency subband image interpolation into the low resolution input images. The sharpness of image is obtained by the estimation high frequency subband. Inverse DWT is performed to reconstruct the resultant image. The visual and mathematical results are presented and discussed on LANDSAT 8 data with comparison of proposed method over conventional and state of art resolution enhancement methods.

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Brain Tumor Segmentation Using Fuzzy C-Means and Tumor Grade Classification Using SVM

Sajja

Kalluri

2020

Lecture Notes in Networks and Systems

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Image enhancement using DT-CWT based cycle spinning methodology

Cited by 6 publications

References 11 publications

RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

An effective method for satellite image enhancement

Brain Tumor Segmentation Using Fuzzy C-Means and Tumor Grade Classification Using SVM

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