Real-Time Biologically-Inspired Image Exposure Correction

Vonikakis, Vassilios; Iakovidou, Chryssanthi; Andreadis, Ioannis

doi:10.1007/978-3-642-12267-5_8

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vonikakis et al [29] presented an image enhancement-based HDR imaging technique. They stretched the luminance value of every frame by building a pipelined structure and implemented their algorithm in Altera's Stratix II.…”

Section: Related Workmentioning

confidence: 99%

HLS Based Approach to Develop an Implementable HDR Algorithm

Saha¹,

Banik²,

Kim³

2018

Electronics

View full text Add to dashboard Cite

Hardware suitability of an algorithm can only be verified when the algorithm is actually implemented in the hardware. By hardware, we indicate system on chip (SoC) where both processor and field-programmable gate array (FPGA) are available. Our goal is to develop a simple algorithm that can be implemented on hardware where high-level synthesis (HLS) will reduce the tiresome work of manual hardware description language (HDL) optimization. We propose an algorithm to achieve high dynamic range (HDR) image from a single low dynamic range (LDR) image. We use highlight removal technique for this purpose. Our target is to develop parameter free simple algorithm that can be easily implemented on hardware. For this purpose, we use statistical information of the image. While software development is verified with state of the art, the HLS approach confirms that the proposed algorithm is implementable to hardware. The performance of the algorithm is measured using four no-reference metrics. According to the measurement of the structural similarity (SSIM) index metric and peak signal-to-noise ratio (PSNR), hardware simulated output is at least 98.87 percent and 39.90 dB similar to the software simulated output. Our approach is novel and effective in the development of hardware implementable HDR algorithm from a single LDR image using the HLS tool.

show abstract

Section: Related Workmentioning

confidence: 99%