Motion-blurred and defocused clips produced from a sharp source with a recorded kernel, so deblurring output can be measured against the original rather than compared to another blurred frame.
Halved frame rate where each output frame is the AVERAGE of the two it replaces, rather than one of them — what a long shutter angle actually produces. Substantially harder than clean decimation, because the interpolator must undo motion blur as well as invent the missing instants, and no input frame matches any ground-truth frame exactly.
Halved frame rate where each output frame is the AVERAGE of the two it replaces, rather than one of them — what a long shutter angle actually produces. Substantially harder than clean decimation, because the interpolator must undo motion blur as well as invent the missing instants, and no input frame matches any ground-truth frame exactly.
Halved frame rate where each output frame is the AVERAGE of the two it replaces, rather than one of them — what a long shutter angle actually produces. Substantially harder than clean decimation, because the interpolator must undo motion blur as well as invent the missing instants, and no input frame matches any ground-truth frame exactly.
The sharp reference for the detail-chart deblur set. Each blurred clip in this group applies a documented kernel to these exact pixels, so the deconvolution problem has a known answer — including which detail was destroyed outright and therefore cannot be recovered, only invented.
Horizontal motion blur, 9 px applied to the sharp detail-chart plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
Horizontal motion blur, 21 px applied to the sharp detail-chart plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
Defocus blur, radius 5 applied to the sharp detail-chart plate with a Gaussian kernel — a defocused lens. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
The sharp reference for the text-motion deblur set. Each blurred clip in this group applies a documented kernel to these exact pixels, so the deconvolution problem has a known answer — including which detail was destroyed outright and therefore cannot be recovered, only invented.
Horizontal motion blur, 9 px applied to the sharp text-motion plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
Horizontal motion blur, 21 px applied to the sharp text-motion plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
Defocus blur, radius 5 applied to the sharp text-motion plate with a Gaussian kernel — a defocused lens. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
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