(1) Field of the Invention
The present invention relates to an image processing method, an image processor, an integrated circuit, and a recording medium for generating an output image of a resolution higher than a resolution of an input image, using the input image.
(2) Description of the Related Art
When a resolution of an image to be displayed on a high resolution display is lacking, it is necessary to enlarge the image so as to match the resolution of the image to a resolution of the high resolution display.
Although methods of increasing the resolution of an image have been conventionally suggested, there is a limit on processing capacity for the practical use. Thus, the combination of simple image enlargement by interpolation and image enhancement processing has supported the methods. Thus, problems of image degradation have occurred, such as blurring and noticeable jaggies in an edge.
Recent technological advances in performance of hardware has enabled allocation of larger processing capacity to the image enlargement processing. Here, attention is currently focused on the super-resolution technique capable of converting an image to an image with high quality and high resolution through complex processing.
In the super-resolution technique, a method using a training database storing data learned from examples of correspondence between high-resolution images and low-resolution images is called a training-based super-resolution.
FIG. 21 is a block diagram illustrating a configuration of an image processor 600 that performs the same processing as in the conventional training-based super-resolution disclosed in Patent Reference 1. Hereinafter, the technique disclosed in Patent Reference 1 (Japanese Unexamined Patent Application Publication No. 2003-018398) is also referred to as a conventional technique A.
As illustrated in FIG. 21, the image processor 600 includes a training database 610, an image enlargement unit 605, a search vector generating unit 620, a training data search unit 630, and an addition unit 680.
Next, operations of the image processor 600 will be described.
FIG. 22 is a flowchart of the procedure performed by the image processor 600 in FIG. 21.
The image processor 600 converts an input image 601 of a low resolution into an output image 602 of a high resolution, using the training database 610 that stores index vectors and high-frequency component data items 631 that are respectively associated with the index vectors. Each of the index vectors is an image of a low-frequency component.
The training database 610 stores the index vectors and the high-frequency component data items 631 that are respectively associated with the index vectors. Each of the index vectors and the high-frequency component data items 631 is a block-shaped image.
At Step S4001, the image enlargement unit 605 generates a temporary enlarged image 606 by enlarging the input image 601.
The following processes are performed on each target block included in the input image 601.
At Step S4002, the search vector generating unit 620 generates a search vector 621 by concatenating a medium-frequency component data item of a target block extracted from the temporary enlarged image 606, with a high-frequency component data item of an adjacent block which has been processed. In other words, the search vector 621 is composed of the medium-frequency component data item and the high-frequency component data item in an overlap region with the adjacent block. The search vector 621 is a block-shaped image.
At Step S4003, the training data search unit 630 selects one of the high-frequency component data items 631 that corresponds to an index vector having the highest similarity to the search vector 621, from among the index vectors stored in the training database 610.
At Step S4004, the addition unit 680 adds the selected high-frequency component data item 631 to the temporary enlarged image 606. Thereby, an output image 602 of a high resolution is generated.
In the conventional training-based scheme, one of the high-frequency component data items 631 that corresponds to the index vector having the highest similarity to the search vector 621 is searched. Since only one of the high-frequency component data items 631 is selected from the database in this method, when the high-frequency component data item 631 selected from the training database 610 indicates noise, the noise immunity is low.
Thus, since a bumpy edge and artifacts occur in the output image 602 of the high resolution, the image quality is degraded. Furthermore, since the search vector 621 includes the overlap region included in a high-frequency component and in the adjacent region, there is a possibility of error propagation.