Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Fusion methods based on dynamic-segmented morphological wavelet or cut and paste for multifocus images

This paper presents a dynamic-segmented morphological wavelet fusion method (DSMWF) and a dynamic-segmented cut and paste fusion method (DSCP). Non-focus regions tend to spread around within multifocus images. The proposed methods firstly divide each multifocus image into segments and. select each sharpest segment at the same location within all images as the "focus segment", based on DCT spectrum concentration on high-frequency sub-band. Each focus segment is further divided into smaller blocks having uniform visual complexity d based on Laplacian edge density. Finally, method DSMWF applies a single-level variable size morphological wavelet fusion method to each block of 2(d) x 2(d) and method DSCP applies direct cut and paste of the sharpest block to each block of 2(d) x 2(d), respectively, to obtain a fused image. The experimental results demonstrate that (a) the PSNR of the fused image using DSMWF is 2-3dB better than that of MMWF in an average, (b) the occurrence of reconstructing both pixels with position error and underflow value is greatly reduced with DSMWF, (c) the performance of DSCP is much superior to that of both MMWF and DSMWF, and (d) block sharpness assessment based on DCT spectrum concentration on high frequency sub-band performs better than DWT and Laplacian edge for this application. (c) 2008 Elsevier B.V. All rights reserved

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome