The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR’s features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping (TM) operators, have been studied and proposed in the last few decades. In this article, we present a comprehensive survey of 60 TM algorithms that have been implemented on hardware for acceleration and real-time performance. In this state-of-the-art survey, we will discuss those TM algorithms which have been implemented on GPU, FPGA, and ASIC in terms of their hardware specifications and performance. Output image quality is an important metric for TM algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the quality of those algorithms hardware implementation. We have compiled those metrics used in this survey, and analyzed the relationship between hardware cost, image quality and computational efficiency. Currently, machine learning-based (ML) algorithms have become an important tool to solve many image processing tasks, and this article concludes with a discussion on the future research directions to realize ML-based TM operators on hardware.

There are many compelling characteristics of signals in the terahertz band (100GHz to 10THz) located between the millimeter wave band and the infrared band. In particular, terahertz waves have higher spatial resolution than mm-waves. Moreover, they can transmit through various substances such as plastics, fibers and paper, and can detect hazardous substances. Because of these features, interest in terahertz applications such as security screening is rising. However, there is a paucity of low-cost terahertz detectors. The Si-CMOS process technology is low-cost and highly integratable with readout electronics and on-chip signal processors. A key consideration for many of the terahertz-detector technologies is the need for additional process steps to make them compatible with CMOS technologies [1]. Recent antenna-type pixel detectors have shown success in high-speed operation and do not require extra process steps [2]; however, power consumption of each pixel and the sequential read-out architecture offsets the speed advantage.

We present a fast global and locally adaptive tone mapping algorithm and its FPGA implementation. The specially designed tone mapping function, which is based on local histogram equalization, controls global and local characteristics individually. In contrast to other tonemap operators, our algorithm manages light/dark halos separately and by using local tonemap function alone, it is able to effectively suppress noise. We validated our algorithms effectiveness by means of subjective and objective assessment. Using average of the bins, we achieve fast smoothed local histogram estimation with fewer bins while maintaining high accuracy. Our new implementation method, requires minimal data access and reduced memory as it operates with a downscaled frame size of 240 × 135 pixels. Relative local area size is 248 × 248 @Full-HD resolution (1920 × 1080). For low-latency pixel output, the system performs the tone mapping using pixel information from the previous frame. When we implemented the system on FPGA (TB-7K-325TIMG, Xilinx Kintex-7), we achieved lightweight hardware as the total usage rate is about 25% of the available FPGA resource. Using an online 1080p video we demonstrate, a real-time video processing using our hardware tone mapping system.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrists and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolution of radiographic imaging, joint space narrowing (JSN) progression of RA for the same reason above can be less than one pixel per year with universal spatial resolution. Insensitive monitoring of JSN can hinder the radiologist/rheumatologist from making a proper and timely clinical judgment. In this paper, we propose a novel and sensitive method that we call partial image phaseonly correlation which aims to automatically quantify JSN progression in the early RA. The majority of the current literature utilizes the mean error, root-mean-square deviation and standard deviation to report the accuracy at pixel level. Our work measures JSN progression between a baseline and its follow-up finger joint images by using the phase spectrum in the frequency domain. Using this study, the mean error can be reduced to 0.0130 mm when applied to phantom radiographs with ground truth, and 0.0519 mm standard deviation for clinical radiography. With the subpixel accuracy far beyond usual manual measurements, we are optimistic that the proposed work is a promising scheme for automatically quantifying JSN progression.