Enhancement of contrast and resolution of gray scale and color images by wavelet decomposition and histogram shaping and shifting

Gourav,; Singh, Shiv

doi:10.1109/medcom.2014.7006022

Cited by 2 publications

(1 citation statement)

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“…The low-frequency components affect the image base content, such as contrast, contour, and structure, while the high-frequency components affect the image details. For this reason, the high-frequency and low-frequency components of the image are processed separately to achieve excellent reconstruction results in many image enhancement methods [ 18 , 19 , 20 , 21 ]. Similarly, contrast and noise are difficult to be handled separately when degrading images in the spatial domain, which may make degradation from HR to LR more challenging.…”

Section: Introductionmentioning

confidence: 99%

Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

Qu,

Chen,

et al. 2024

Entropy

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The supervised super-resolution (SR) methods based on simple degradation assumptions (e.g., bicubic downsampling) have unsatisfactory generalization ability on real-world thermal images. To enhance the SR effect of real-world sceneries, we introduce an unsupervised SR framework for thermal images, incorporating degradation modeling and corresponding SR. Inspired by the physical prior that high frequency affects details and low frequency affects thermal contrast, we propose a frequency-aware degradation model, named TFADGAN. The model achieves image quality migration between thermal detectors of different resolutions by degrading different frequency components of the image from high-resolution (HR) to low-resolution (LR). Specifically, by adversarial learning with unpaired LR thermal images, the complex degradation processes of HR thermal images at low and high frequencies are modeled separately. Benefiting from the thermal characteristics mined from real-world images, the degraded images generated by TFADGAN are similar to LR thermal ones in terms of detail and contrast. Then, the SR model is trained based on the pseudo-paired data consisting of degraded images and HR images. Extensive experimental results demonstrate that the degraded images generated by TFADGAN provide reliable alternatives to real-world LR thermal images. In real-world thermal image experiments, the proposed SR framework can improve the peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM) by 1.28 dB and 0.02, respectively.

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Section: Introductionmentioning

confidence: 99%

Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

Qu,

Chen,

et al. 2024

Entropy

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A Hybrid Technique of Segmentation for Optimizing Features in Medical Images

Savita¹,

Ada²

2016

IJCA

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Image segmentation is a part of digital image processing that deals to extract hidden patterns from the medical images. Medical image segmentation is a technique using to mean manually, fully or semi-automatically delineating the boundaries of tissue regions or an object. It is a process of changing the representation step of an image into some extent which is easy to analyze. In the recent research, cluster based, region based, threshold based and edge based approaches have been proposed. These approaches provide relational features from the image that provide various types of information. In medical science, image segmentation provides various achievements for decision making process. In the proposed work, image segmentation has been done using Hybrid PSO with multi scale super resolution approach. In the process of MSR, super resolution model has been implemented that computes gradient and sobel edges. After MSR, particle swarm optimization has been implemented that use fitness factor for computation of global fitness .After all the iterations and on the basis of global value, image has been segmented. Various parameters like PSNR, MSE, GCE, Random index, Variance and computation time have been measured for performance evaluation. The parameters show that proposed approach provides better results.

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Enhancement of contrast and resolution of gray scale and color images by wavelet decomposition and histogram shaping and shifting

Cited by 2 publications

References 5 publications

Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution

A Hybrid Technique of Segmentation for Optimizing Features in Medical Images

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