Proposed Title :

• A Real-Time FHD Learning Based Super Resolution System Without a Frame Buffer

Improvement of this Project:

• Increases the resolution 800×800 images to 1600×1600 image size with Vertical and Horizontal Interpolation technique using Bi-cubic method and find out SSIM and PSNR.

• Configure Patch Feature Extraction and Dictionary Correlation with Kernel Coefficient method.

Software implementation:

• Modelsim
• Xilinx 14.2

Proposed System:

The main aim of the Single image (SR) super-resolution is to generate (HR) high-resolution images from (LR) low-resolution  images.  This paper briefly presents a concept of real time super resolution method of FHD based image extended and scaling processor.  The super resolution system includes three blocks of operations.  The first is a low-frequency interpolation stage, where bicubic interpolation is used for reconstructing the low-frequency parts of HR images. The second stage generates high-frequency patches by choosing the highest related pre-trained regression function according to each HR low frequency patch. In the third stage, with the high-frequency information, the low-frequency image patches are enhanced and overlapped to construct the SR result. These operations for gaining a high-frequency result are applied to the Y-luminance channel only, while the high-resolution Cb and Cr channels are generated by bicubic interpolation. The proposed system generates the output image resolution of 1920 X 1080 (FHD) by the input of 800 X 800 image size. The proposed architecture performs an anchored neighborhood regression algorithm that generates a high-resolution image from a low-resolution image input using only numbers of line buffers.  Finally, super resolution technique is implemented in VHDL  and Synthesized in the XILINX VERTEX-5 FPGA and shown the comparison for power, area and delay reports.

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A Real-Time FHD Learning Based Super Resolution System Without a Frame Buffer

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