If the higher resolution frames are encoded, then N and M might increase.
The high-resolution frame is 480 × 640 pixels.
Super-resolution is an imaging technique that uses multiple low-resolution frames to construct a high-resolution frame.
Finally, all of these difference frames are combined and enlarged back to the high-resolution frame size.
Figure 2 shows a comparison using bicubic interpolation and the super-resolution algorithm to generate a high-resolution frame.
This is an iterative feedback algorithm in which the pixel information of several low-resolution frames is combined to form a single high-resolution frame.
Super-resolution is an imaging technique that leverages motion blur and multiple low-resolution frames to construct a high-resolution frame.
The floating point pixel calculation module performs the algorithm outlined in Figure 1 and writes the resulting high-resolution pixel back to the portion of the M4k memory block reserved for the final high-resolution frame.
Note that none of the low-resolution frames are exactly identical; they are all different, and thus, the algorithm leverages on this fact to extract the details from each low-resolution frame and combine them to form a single high-resolution frame.
The main portions of the code consist of transferring the low-resolution frames and resulting high-resolution frame between the host (computer) to the device (GPGPU), writing the kernel to do the frame resizing, writing the kernel to compare and generate the error frame, and writing a kernel to adjust the hypothesis with the error frame data.
In a following paper, they focused on the developing of the spatial resolution enhancement implementation on the FPGA - namely an iterative back projection method in which low-resolution frames are loaded to memory and read for processing to generate a high-resolution frame [15].
