Speckle noise is one of the most critical disturbances that alter the quality of Synthetic Aperture Radar (SAR) coherent images. Before using SAR images in automatic target detection and recognition, the first step is to reduce the effect of speckle noise. Several adaptive and non-adaptive filters are widely used for despeckling in SAR images. In this paper, an adaptive mathematical morphological filter is proposed to reduce the speckle noise in SAR images. The new filter performance is compared with a number of despeckling filters with different parameters. For performance measurements, several parameters were evaluated to test the filter ability to attenuate the speckle noise and keep target information. From experimental results, the new proposed morphological filter gives promising results for significantly suppressing speckle noise and preserving the potential targets.

A new edge-guided morphological filter is proposed to sharpen digital images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of other sharpener-type filters.

During the past years, face and gait recognition in video have received significant attention. Consequently, their recognition problems have challenged due to largely varying appearances and highly complex pattern distributions. However, the complementary properties of these two biometrics suggest fusion of them. Face recognition is more reliable when the person is close to the camera. On the other hand, gait is a suitable biometric trait for human recognition at a distance. Information from these two biometric sources, frontal of face and side of gait, are utilized and integrated at feature level. Face image is represented by the Active Lines among Face Landmark Points (ALFLP) feature vector. Gait image is represented by the Active Horizontal Levels (AHL) feature vector. Face and gait feature vectors are fused using a proposed effective fusion method. The proposed system was tested on CASIA database and the achieved results showed that the integrated face and gait features carry the most discriminating power compared to any individual biometric.

Synthetic aperture radar (SAR) images depend on the dielectric properties of objects with certain incident angles. Thus, vessels and other metallic objects appear clear in SAR images however, they are difficult to be distinguished in optical images. Synergy of these two types of images leads to not only high spatial and spectral resolutions but also good explanation of the image scene. In this paper, a hybrid pixel-level image fusion method is proposed for integrating panchromatic (PAN), multispectral (MS) and SAR images. The fusion method is performed using Multi-stage guided filter (MGF) for optical images pansharpening, to get high preserving spatial details and nested Gram-Schmidt (GS) and Curvelet-Transform (CVT) methods for SAR and optical images,to increase the quality of the final fused image and benefit from the SAR image properties. The accuracy and performance of the proposed method are appraised using Landsat-8 Operational-Land-Imager (OLI) and Sentinel-1 images subjectively as well as objectively using different quality metrics. Moreover, the proposed method is compared to a number of state-of-the-art fusion techniques. The results show significant improvements in both visual quality and the spatial and spectral evaluation metrics. Consequently, the proposed method is capable of highlighting maritime activity for further processing.

One of the most common degradations in aerial images is their poor contrast and low resolution quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen aerial images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in aerial images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters.