Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.
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As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful label-free, chemical-specific technique for biomedical and mineralogical imaging which can suffer from low signal-to-noise ratios due to requirements of low input laser power or fast imaging, or from optical scattering and low target concentration.
The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively.
To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement.
We went below the MRI acceleration factors (a. k. a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered powerful deep learning based image enhancement methods to compensate for the underresolved images.
Based on this process and results, K-means segmentation based on Lab color space can be used for the initial stages of the embryo detection process.
We believe that framework might be very useful in the prediction of color palletes of the Renaissance oil artworks and other artworks.
The egg embryos detection is processed using a segmentation process.
Two sets of results show the effectiveness of the proposed algorithms when compared to traditional and state-of-the-art methods.