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Our benchmark is the first to cover the important use case of hand-held mobile devices while also providing high-resolution DSLR camera images.
We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.
As a convenient communication tool for everyday use, the principal advantages of a camera phone are its low cost, easy accessibility, and compactness.
Because the ultimate resolution of the camera is fully utilised, this method has a high sensitivity and accuracy in the distortion measurement.
As this process is computationally intensive, we propose a camera system that uses the spatial and temporal information measures SI and TI standardized by ITU as camera parameters to determine during capture whether super-resolution processing would result in an increase in perceived quality.
The subjective and objective data show that photospace conditions have a much bigger impact on image quality of a camera phone than the pixel count of its imager.
Some experiments show that the correlation between objectives measurements derived from this target and subjective measurements conducted in the Camera Phone Image Quality initiative are excellent.
This paper presents an image blur reduction algorithm for cell-phone cameras having a low computational complexity and without making any assumption about the handshake motion.
By not needing frame memory, the approach is feasible for integration into the size-constrained image sensors of cell phone cameras.
Our analysis also identified camera frame rate jitter as a major source of variability and error across different phone models, but this can be largely corrected through non-linear resampling.