In the unsharp masking approach for image enhancement, a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version. The method is simple, but it suffers from two serious drawbacks. First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas. Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images. In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges. On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification. A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters. This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters. Computer simulations have verified the superior results obtained using these filters. In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods.

Hardware accelerators based on field programmable gate array (FPGA) and system on chip (SoC) devices have gained attention in recent years. One of the main reasons is that these devices contain reconfigurable logic, which makes them feasible for boosting the performance of applications. High-level synthesis (HLS) tools facilitate the creation of FPGA code from a high level of abstraction using different directives to obtain an optimized hardware design based on performance metrics. However, the complexity of the design space depends on different factors such as the number of directives used in the source code, the available resources in the device, and the clock frequency. Design space exploration (DSE) techniques comprise the evaluation of multiple implementations with different combinations of directives to obtain a design with a good compromise between different metrics. This paper presents a survey of models, methodologies, and frameworks proposed for metric estimation, FPGA-based DSE, and power consumption estimation on FPGA/SoC. The main features, limitations, and trade-offs of these approaches are described. We also present the integration of existing models and frameworks in diverse research areas and identify the different challenges to be addressed.

The linear unsharp masking technique used in image contrast enhancement is modified in this article by introducing a control term expressed as a rational function of the local input data. In this way noise amplification is avoided and, at the same time, overshoot effects on sharp edges are limited. Experimental results support the validity of the method, even as a preprocessor for interpolation systems.

When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped—0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications.

We propose a new algorithm to digitally restore vintage photographic prints affected by foxing and water blotches. It semiautomatically recovers the defects utilizing the features of the stains. The restoration process enhances the residual image information still present in the area. It is composed of three different steps: inpainting, additive-multiplicative (A-M) modeling, and interpolation.