TL;DR
A novel image segmentation model that integrates Retinex theory within a hybrid-order total variation framework is proposed that effectively mitigates staircase artifacts and retains structural details, resulting in more natural image restorations and improved segmentation accuracy.
Image reconstruction aims to restore missing parts of the image while keeping geometry and quality intact. The traditional second-order partial differential equation (PDE) and Total Variation (TV) methods can be used to perform such an action; nonetheless, their disadvantages, such as over-smoothing, staircase effect,...
In practice, active contour models (ACMs) are effective in segmenting images, which are mostly applied in computer vision and medical imaging. However, the result may be challenged when the images contain noise and intensity inhomogeneity. Images are commonly deteriorated by a mixture of noise involving additive Gaussi...
A multi-scale image fusion framework is proposed, which leverages adaptive exposure, color gradients, and contrast, and demonstrates consistent improvements over state-of-the-art methods across multiple metrics.
Prior-Guided Histogram Equalization (PGHE), a lightweight enhancement framework that integrates conventional HE with Retinex-based illumination priors, and functions as a plug-in module that improves existing HE variants with measurable gains in Structural Similarity and perceived naturalness at a modest cost in Absolu...
Multi-Exposure Image Fusion (MEF) aims to fuse a single visually symmetrical image, which combines the luminosity and preserves the structural data of a scene at different exposure levels. Traditionally, Laplacian Pyramid (LP) fusion methods rely on Gaussian smoothing, which diffuses intensity isotropically, resulting...
Many studies rely heavily on the 3-D reconstruction of optical microscope (OM) images; however, conventional approaches such as Z-stacking are limited to a single viewing angle, which inevitably results in visual blind spots. Moreover, OM systems often struggle to reliably match keypoints when processing samples with w...
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