SUPER-RESOLUTION IMAGE USING SELECTED EDGE PIXELS
A method of providing a super-resolution image is disclosed. The method uses a processor to perform the following steps of acquiring a captured low-resolution image of a scene and resizing the low-resolution image to provide a high-resolution image. The method further includes computing local edge parameters including local edge orientations and local edge centers of gravity from the high-resolution image, selecting edge pixels in the high-resolution image responsive to the local edge parameters, and modifying the high-resolution image in response to the selected edge pixels to provide a super-resolution image.
1 . A method of providing a super-resolution image, comprising using a processor to perform the following:
(a) acquiring a captured low-resolution image of a scene;
(b) resizing the low-resolution image to provide a high-resolution image;
(c) computing local edge parameters including local edge orientations and local edge centers of gravity from the high-resolution image;
(d) selecting edge pixels in the high-resolution image responsive to the local edge parameters; and
(e) modifying the high-resolution image in response to the selected edge pixels to provide a super-resolution image.
2 . The method of claim 1 wherein step (b) further includes increasing the resolution of the image using bicubic interpolation.
3 . The method of claim 1 wherein step (b) further includes increasing the resolution of the image using bilinear interpolation.
4 . The method of claim 1 wherein step (d) further includes selecting edge pixels in either horizontal, vertical, or diagonal directions.
5 . The method of claim 1 wherein step (e) further includes converting the high-resolution image from red-green-blue (RGB) color space to luminance-chrominance (YCC) color space, modifying the luminance channel, and converting the modified high-resolution image back into RGB color space.
6 . The method of claim 1 wherein step (e) further includes providing a low-pass image and a high-pass image from the high-resolution image, modifying the high-pass image, and combining the low-pass image and the modified high-pass image to produce the super-resolution image.
7 . The method of claim 6 further including providing a low-pass image and a high-pass image from the high-resolution image using pyramid decomposition.
8 . The method of claim 6 further including providing a low-pass image and a high-pass image from the high-resolution image using convolution.
9 . The method of claim 1 wherein step (e) further includes modifying the high-resolution image with global sharpening or with adaptive sharpening.
10 . The method of claim 1 wherein step (e) further includes modifying the high-resolution image with global deblurring or with adaptive deblurring.
11 . The method of claim 1 wherein step (e) further includes modifying the high-resolution image by scaling the selected edge pixels by a gain factor.
12 . The method of claim 1 wherein step (e) further includes computing a first contrast before modifying the high-resolution image, a second contrast after modifying the high-resolution image, and modifying the high-resolution image responsive to the first contrast and the second contrast.
13 . The method of claim 1 wherein step (e) further includes modifying the high-resolution image by scaling the selected edge pixels responsive to the local edge parameters.
14 . A method of providing a super-resolution image, comprising using a processor to perform the following:
(a) acquiring a captured low-resolution image of a scene;
(b) modifying the low-resolution image to provide a sharpened low-resolution image;
(c) resizing the sharpened low-resolution image to provide a high-resolution image;
(d) computing local edge parameters including local edge orientations and local edge centers of gravity from the high-resolution image;
(e) selecting edge pixels in the high-resolution image responsive to the local edge parameters; and
(f) modifying the high-resolution image in response to the selected edge pixels to provide a super-resolution image.
15 . A method of providing a super-resolution image, comprising using a processor to perform the following:
(a) acquiring a captured low-resolution image of a scene;
(b) decomposing the low-resolution image into pyramid image components;
(c) modifying the pyramid image components to provide sharpened pyramid image components;
(d) reconstructing a sharpened low-resolution image from the sharpened pyramid image components;
(e) resizing the sharpened low-resolution image to provide a high-resolution image;
(f) computing local edge parameters including local edge orientations and local edge centers of gravity from the high-resolution image;
(g) selecting edge pixels in the high-resolution image responsive to the local edge parameters; and
(h) modifying the high-resolution image in response to the selected edge pixels to provide a super-resolution image.