Comparing Adobe’s Unsharp Masks and High-Pass Filters in Photoshop Using the Visual Information Fidelity Metric

The present study examines image sharpening techniques quantitatively. A technique known as unsharp masking has been the preferred image sharpening technique for imaging professionals for many years. More recently, another professional-level sharpening solution has been introduced, namely, the high-pass filter technique of image sharpening. An extensive review of the literature revealed no purely quantitative studies that compared these techniques. The present research compares unsharp masking (USM) and high-pass filter (HPF) sharpening using an image quality metric known as Visual Information Fidelity (VIF). Prior researchers have used VIF data in research aimed at improving the USM sharpening technique. The present study aims to add to this branch of the literature through the comparison of the USM and the HPF sharpening techniques. The objective of the present research is to determine which sharpening technique, USM or HPF, yields the highest VIF scores for two categories of images, macro images and architectural images. Each set of images was further analyzed to compare the VIF scores of subjects with high and low severity depth of field defects. Finally, the researcher proposed rules for choosing USM and HPF parameters that resulted in optimal VIF scores. For each category, the researcher captured 24 images (12 with high severity defects and 12 with low severity defects). Each image was sharpened using an iterative process of choosing USM and HPF sharpening parameters, applying sharpening filters with the chosen parameters, and assessing the resulting images using the VIF metric. The process was repeated until the VIF scores could no longer be improved. The highest USM and HPF VIF scores for each image were compared using a paired t-test for statistical significance. The t-test results demonstrated that: • The USM VIF scores for macro images (M = 1.86, SD = 0.59) outperformed those for HPF (M = 1.34, SD = 0.18), a statistically significant mean increase of 0.52, t = 5.57 (23), p = 0.0000115. Similar results were obtained for both the high severity and low severity subsets of macro images. • The USM VIF scores for architectural images (M = 1.40, SD = 0.24) outperformed those for HPF (M = 1.26, SD = 0.15), a statistically significant mean increase of 0.14, t = 5.21 (23), p = 0.0000276. Similar results were obtained for both the high severity and low severity subsets of architectural images. The researcher found that the optimal sharpening parameters for USM and HPF depend on the content of the image. The optimal choice of parameters for USM depends on whether the most important features are edges or objects. Specific rules for choosing USM parameters were developed for each class of images. HPF is simpler in the fact that it only uses one parameter, Radius. Specific rules for choosing the HPF Radius were also developed for each class of images. Based on these results, the researcher concluded that USM outperformed HPF in sharpening macro and architectural images. The superior performance of USM could be due to the fact that it provides more parameters for users to control the sharpening process than HPF

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

Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature have been focusing on the Single-Image Super-Resolution problem so far. At present, satellite based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for single or multi-image super-resolution, have demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-Image Super-Resolution for remote sensing applications

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others