Image processing apparatus using the difference among scaled images as a layered image and method thereof

Abstract

The present invention is to provide an image processing apparatus using the difference among scaled images as a layered image and a method thereof, which utilize the Gaussian and Laplacian pyramid theory to convert an original image into a plurality of scaled images of different scales, and the difference among scaled images of two adjacent different scales as a layered image of the corresponding layer, so that the edge and line characteristics of a scene of the original image for each layered image can be displayed in different levels sequentially from a clear level to a vague level, and provide a layered image display interface and an image characteristic editing interface for users to examine each layered image through the layered image display interface and edit or perform special effect to each layered image, so as to simulate different visual effects based on different vision models.

Description

FIELD OF THE INVENTION[0001]The present invention relates to an image processing apparatus and method, more particularly to an image processing apparatus and method by using the difference among scaled images as a layered image for enabling a user to edit or perform special effect to the edge and line characteristics of the layered images of a scene of an original image, so as to simulate different visual effects based on different vision models.BACKGROUND OF THE INVENTION[0002]In general, an image processing software usually comes with a layer processing function that allows each image to be resided at a layer all the time as shown in FIG. 1, and thus users usually need to create a layer 111, 121 or add a layer before processing (including editing or composing) an image 110, 120 in a file, and a canvas 130 is situated at the lowest layer of all layers 111, 121 but the canvas 130 is not a layer by itself. Therefore, the layers 111, 121 are similar to a stack of transparent films, an...

AI Technical Summary

Benefits of technology

[0010]A further objective of the present invention is to provide a layered image editing interface, and the layered image editing interface includes a layered image editing window for displaying each editing layered image or a composed image composed of the layered images, and its periphery has at least one layered image characteristic adjusting button for users to click to adjust the contrast or Gaussian variance of the layered image; at least one size adjusting button for users to click to zoom in or out the layered image or composed image; and at least one image composition switch button for users to click to switch the layered image editing window and select each layered image or composed image, so that a user can click the image composition switch button to browse each editing layered image or composed image.

Problems solved by technology

From the description above, the image processing software available in the market can use the concept and technique of a layer to provide a tool for editing the image on each layer, or rearranging the image sequence of each layer, and can use the image composition technology to simulate a digital dark room, for editing and composing images, but these image processing software cannot show the effect of different vision models for the image.

Although neurophysists already have relatively high understanding on the visual imaging method of a retinal-cortical system of human eyes, yet this understanding is limited to an edge enhancement effect only, since the ganglion cells of different sizes are distributed increasingly denser from a fovea to the peripheral areas of a cornea, and thus the basic visual imaging principle decreases the vision from the center of the visual line to the peripheral areas.

Regardless of the computation program used by these image processing software, the core technology uses the Laplacian of Gaussian edge enhancement technology and concept to simulate the effects of a receptive field of a human vision, but the computation carries out a single-level processing for the image only, and it cannot show the effect of different vision models for the image.

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