The resolution of a low-resolution image is made high and a stereoscopic image is displayed. Resolution is made high by super-resolution processing. In this case, the super-resolution processing is performed after edge enhancement processing is performed. Accordingly, a stereoscopic image with high resolution and high quality can be displayed. Alternatively, after image analysis processing is performed, edge enhancement processing and super-resolution processing are concurrently performed. Accordingly, processing time can be shortened.

The present invention relates to a parallel image measurement method oriented to the insulating layer thickness of a radial symmetrical cable section. The method conducts the non-contact high-accuracy measurement based on the machine vision and the image analysis, adopts a GPU multi-core parallel platform for the high-speed measurement, extracts the useful information from the section image of the radial symmetrical cable, and then measures the insulating layer thickness. Compared with the prior art, the present patent can lower the time consumed for the accurate measurement, fill in the blank of the high-accuracy parallel image measurement of the insulating layer thickness of the radial symmetrical cable section in the domestic cable industry, break down the monopoly and technology blockade by related foreign manufacturers and improve the technology level of on-line testing of product quality in China, expedite the production automation progress of domestic manufacturer.

An image processing apparatus includes: an acquisition unit configured to obtain image data generated by an image sensor forming an array pattern using color filters having mutually different spectral transmittances and to obtain a correction coefficient for correcting a difference between pixel values corresponding to a difference between a spectral sensitivity and a preset reference spectral sensitivity in a wavelength range in a pixel-of-interest; a correction amount calculation unit configured to calculate an estimation value of a color component in the pixel-of-interest using a pixel value of each of pixels in surrounding pixels of a same color and the correction coefficient and configured to calculate a correction amount of the pixel value of the pixel-of-interest based on the estimation value and the correction coefficient of the pixel-of-interest; and a pixel value correction unit configured to correct the pixel value of the pixel-of-interest based on the calculated correction amount.

An interlaced video signal can include content of different types, such as interlaced content and progressive content. The progressive content may have different cadences according to the ratio between the frame rate of the progressive content and the field rate of the interlaced video signal. Cadence analysis is performed to identify the cadence of the video signal and/or to determine field pairings when progressive content is included. As described herein, motion information (e.g. motion vectors) for blocks of fields of a video signal can be used for the cadence analysis. The use of motion information provides a robust method of performing cadence analysis.

Obtaining a restored frame from a degraded frame includes obtaining, for a pixel of the degraded frame, magnitude features based on a first window centered at the pixel. A cardinality N of the magnitude features is at least 1. The magnitude features are used to obtain a pixel-adaptive filter. The pixel-adaptive filter is applied to the pixel to obtain a pixel of the restored frame.

A low-resolution image is displayed at high resolution and power consumption is reduced. Resolution is made higher by super-resolution processing. Then, display is performed with the luminance of a backlight controlled by local dimming after the super-resolution processing. By controlling the luminance of the backlight, power consumption can be reduced. Further, by performing the local dimming after the super-resolution processing, accurate display can be performed.

A method for image-based dynamic feature enhancement includes: using the camera to capture multiple images, selecting an ROI from each of the multiple images displayed on the camera through the ROI selection unit, configuring multiple brightness weights to correspond to the ROI, adjusting brightness of the image according to the multiple brightness weights and multiple actual brightness values corresponding to the ROI, and performing a multi-layer progressive sharpening process to adjust multiple actual sharpening parameters of the image through the sharpening adjustment unit. The camera parameters of the camera can be directly and instantly adjusted to improve brightness and sharpening of images captured by the camera and facilitate advanced driver assistance system to clearly identify road conditions.

The resolution of a low-resolution image is made high and a stereoscopic image is displayed. Resolution is made high by super-resolution processing. In this case, the super-resolution processing is performed after edge enhancement processing is performed. Accordingly, a stereoscopic image with high resolution and high quality can be displayed. Alternatively, after image analysis processing is performed, edge enhancement processing and super-resolution processing are concurrently performed. Accordingly, processing time can be shortened.

In an image processing apparatus that can provide, to an image, an emphasis effect that serves as a focusing degree indicator, provision of an emphasis effect that is easily distinguished is realized. A signal generation circuit generates signals having values that correspond to the values of process target pixels of image signals. For example, based on the magnitude of a frequency component extracted from the image signals by an extraction circuit, a signal modification circuit applies signals to the process target pixels and outputs the process target pixels. The signal generation circuit generates signals so as to have values that decrease continuously or in a stepwise manner in accordance with increase in the values of the process target pixels.

An interlaced video signal can include content of different types, such as interlaced content and progressive content. The progressive content may have different cadences according to the ratio between the frame rate of the progressive content and the field rate of the interlaced video signal. Cadence analysis is performed to identify the cadence of the video signal and/or to determine field pairings when progressive content is included. As described herein, motion information (e.g. motion vectors) for blocks of fields of a video signal can be used for the cadence analysis. The use of motion information provides a robust method of performing cadence analysis.