{"title":"基于FPGA的实时图像去雾架构","authors":"Muhammad Naseem Majoka, G. Raja","doi":"10.1109/INTELLISYS.2017.8324357","DOIUrl":null,"url":null,"abstract":"This paper proposes the design and Field Programmable Gate Array (FPGA) architecture of dark channel prior algorithm for image de-hazing. The RGB hazed image is read in the form of 3×3 patches (9 Pixels) and minimum intensity pixels are obtained from each patch. These minimum intensity pixels form a dark vector. Atmospheric light for each color is estimated using this dark vector. Transmission map is computed by atmospheric light. Guided filter is used to refine the transmission map and finally de-hazed image is recovered. The proposed architecture comprises of 6 cores and is implemented usingVerilog HDL and Xilinx ISE Design Suite 14.5. The proposed image de-hazing architecture is validated by comparing the simulation results with MATLAB results. The proposed architecture is synthesized for Xilinx Zynq board, Device XC7Z045, Package FFG900, Speed −3. Synthesis results show that proposed design consumes 24568 Slice registers and 18644 LUTs. Furthermore, the design consumes 71 BRAMs and can operate on Maximum Frequency of142.786MHzwith minimum clock period of 7.003ns.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"FPGA based image de-hazing architecture for real time applications\",\"authors\":\"Muhammad Naseem Majoka, G. Raja\",\"doi\":\"10.1109/INTELLISYS.2017.8324357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes the design and Field Programmable Gate Array (FPGA) architecture of dark channel prior algorithm for image de-hazing. The RGB hazed image is read in the form of 3×3 patches (9 Pixels) and minimum intensity pixels are obtained from each patch. These minimum intensity pixels form a dark vector. Atmospheric light for each color is estimated using this dark vector. Transmission map is computed by atmospheric light. Guided filter is used to refine the transmission map and finally de-hazed image is recovered. The proposed architecture comprises of 6 cores and is implemented usingVerilog HDL and Xilinx ISE Design Suite 14.5. The proposed image de-hazing architecture is validated by comparing the simulation results with MATLAB results. The proposed architecture is synthesized for Xilinx Zynq board, Device XC7Z045, Package FFG900, Speed −3. Synthesis results show that proposed design consumes 24568 Slice registers and 18644 LUTs. Furthermore, the design consumes 71 BRAMs and can operate on Maximum Frequency of142.786MHzwith minimum clock period of 7.003ns.\",\"PeriodicalId\":131825,\"journal\":{\"name\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INTELLISYS.2017.8324357\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Intelligent Systems Conference (IntelliSys)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELLISYS.2017.8324357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
提出了一种用于图像去雾的暗通道先验算法的设计和FPGA结构。RGB模糊图像以3×3 patch (9 Pixels)的形式读取,每个patch获得最小强度像素。这些最小强度像素形成一个暗向量。每种颜色的大气光都是使用这个暗向量来估计的。透射图由大气光计算。利用引导滤波对传输图进行细化,最终恢复去雾图像。该架构由6个核心组成,并使用verilog HDL和Xilinx ISE Design Suite 14.5实现。通过仿真结果与MATLAB结果的对比,验证了所提出的图像去雾结构。所提出的架构是针对Xilinx Zynq板、Device XC7Z045、Package FFG900、Speed−3而合成的。综合结果表明,该设计消耗了24568个Slice寄存器和18644个lut。此外,该设计消耗71个bram,最高工作频率为142.786 mhz,最小时钟周期为7.003ns。
FPGA based image de-hazing architecture for real time applications
This paper proposes the design and Field Programmable Gate Array (FPGA) architecture of dark channel prior algorithm for image de-hazing. The RGB hazed image is read in the form of 3×3 patches (9 Pixels) and minimum intensity pixels are obtained from each patch. These minimum intensity pixels form a dark vector. Atmospheric light for each color is estimated using this dark vector. Transmission map is computed by atmospheric light. Guided filter is used to refine the transmission map and finally de-hazed image is recovered. The proposed architecture comprises of 6 cores and is implemented usingVerilog HDL and Xilinx ISE Design Suite 14.5. The proposed image de-hazing architecture is validated by comparing the simulation results with MATLAB results. The proposed architecture is synthesized for Xilinx Zynq board, Device XC7Z045, Package FFG900, Speed −3. Synthesis results show that proposed design consumes 24568 Slice registers and 18644 LUTs. Furthermore, the design consumes 71 BRAMs and can operate on Maximum Frequency of142.786MHzwith minimum clock period of 7.003ns.