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.

Several morphological transformations to detect noise are introduced. The initial method is a modification of a procedure presented previously in the current literature. The proposals given in the study allow to detect noise in two ways: (i) using a contrast measure and (ii) applying different proximity criteria into several proposed toggle mappings. In the end, two of the proposals given in this study yield a better performance with respect to methods in which this research is based. However, although the methodology to identify noise works adequately, the results are limited due to the use of the structuring element. In Section 4, an image with two types of noise is cleaned. Such image is contaminated with zero mean Gaussian noise with 0.01 variance and 5% of salt and pepper noise. From this experiment, the proposal giving the best performance is selected; subsequently, this is compared with other recent operators as PDEs, wavelets, morphological connected rank max opening and amoebas.

In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Weber's law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions.

In this paper, we use a type of morphological filters, the openings and closings with reconstruction criteria, for image segmentation purposes. Those filters are a class of extended levelings, and they present interesting features for image processing and analysis. These transformations include a criterion that allow us to control the reconstruction process, and they can avoid some of the undesirable effects shown by the classical openings and closings by reconstruction in some situations. In particular, we can limit the excessive flat zones reconstruction that sometimes arises. In addition, we can separate, if desired, certain regions in images. We use these two effects in image segmentation. Among others we use morphological techniques to segment an input image, and we take advantage of the desirable properties of those filters to compute markers or significant regions. We will focus in this work especially on medical images applications.