In this work, the authors develop a working software-based approach named 'linearly quantile separated histogram equalisation-grey relational analysis' for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.
Climatic and atmospheric phenomena – such as haze, fog and smoke – may lead to deterioration of quality and poor scenic clarity of outdoor images. In computer graphics, the authors can model these images as a linear combination of scene radiance, medium transmission and airlight. Several techniques have been proposed to remove the effects of haze from images using this model. The most effective approach for removing the haze effect from a single image is based on dark channel prior. Dark channel prior is based on statistical observation of outdoor images comprising some regions with dark intensity pixels. Here we propose a new l2‐norm‐based prior to generate a dark channel in order to remove the haze from a single‐input image. The dark channel generated using this new prior is more robust and free from the block‐effect. We also propose a statistical technique for airlight estimation of a given image. The proposed technique for modifying the dark channel prior and the airlight estimation are robust techniques as compared with approaches detailed in currently available literature. By combining this modified dark channel and estimated airlight, the haze can be directly removed and a more accurate haze‐free image can be recovered from single‐input hazy image.
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