Showing papers on "Top-hat transform published in 2021"

Visible-NIR Image Fusion Based on Top-Hat Transform

Abstract: The near-infrared band of the electromagnetic spectrum has become an important tool for enhancing image quality. Commonly, outdoor color images are degraded by bad weather conditions that lead to a loss of contrast and fine details in color images since light scattering produces attenuation and smoothing effects. Despite the fact that current Visible-NIR fusion methods achieve image enhancement features, some issues like edge preservation and color oversaturation still need to be addressed. In this work, a method that performs a selective Visible-NIR fusion of the most relevant image structures through top-hat transform is proposed. The performance of the method is evaluated by quantifying the new information added to the image and the change in color. Experimental results show a high degree of detail in preserving the edges while maintaining the color of the image. Moreover, the proposed method demonstrates that the image quality improvements were not significantly affected by a change in the color space.

An index based road feature extraction from LANDSAT-8 OLI images

Abstract: Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.

Retinal Image Enhancement via a Multiscale Morphological Approach with OCCO Filter

Abstract: Retinal images are widely used for diagnosis and eye disease detection However, due to the acquisition process, retinal images often have problems such as low contrast, blurry details or artifacts These problems may severely affect the diagnosis Therefore, it is very important to enhance the visual quality of such images Contrast enhancement is a pre-processing applied to images to improve their visual quality This technique betters the identification of retinal structures in degraded retinal images In this work, a novel algorithm based on multi-scale mathematical morphology is presented First, the original image is blurred using the Open-Close Close-Open (OCCO) filter to reduce any artifacts in the image Next, multiple bright and dark features are extracted from the filtered image by the Top-Hat transform Finally, the maximum bright values are added to the original image and the maximum dark values are subtracted from the original image, previously adjusted by a weight The algorithm was tested on 397 retinal images from the public STARE database The proposed algorithm was compared with state of the art algorithms and results show that the proposal is more efficient in improving contrast, maintaining similarity with the original image and introducing less distortion than the other algorithms According to ophthalmologists, the algorithm, by improving retinal images, provides greater clarity in the blood vessels of the retina and would facilitate the identification of pathologies

Image Enhancement Methods for Remote Sensing: A Survey

Abstract: The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.