Sony Corporation

Sony Electronics Inc

Kenichi Nishio

Farhan A Baqai

Akira Matsui

Alexander Berestov

Ted J Cooper

Hiroyuki Kinoshita

Xiaogang Dong

Atsuo Minato

Ting Zhang

Gentaro Irisawa

Masaya Kinoshita

Takuya Kato

Tsukasa Hashino

Atsuo Kikuchi

Hyongmyong Kang

Nobuyuki Matsushita

Florian Ciurea

Gazi Ali

Jiro Takatori

Hideki Nabesako

Yuko Shibaike

Hiroaki Ebi

Tak Shing Wong

Yohei Myoga

Eiji Mizutani

Various aspects of a method and a system for image processing are disclosed herein. The method includes processing an input image, which comprises structure information and detail information, using image processing (IP) blocks in an image processing pipeline. One or more of the IP blocks, such as the 'lossy' IP blocks, process the input image with at least a partial loss of the detail information. By replacing the 'lossy' IP blocks with redesigned image processing (IP) modules, the image processing pipeline reduces or avoids such loss of the detail information. A more efficient implementation of the improved pipeline is realized by using a master IP module when the &#x201c;lossy&#x201d; IP blocks are reordered and grouped together in the image processing pipeline. The method is further extended to process 3-D images to reduce or avoid loss of detail information in a 3-D image processing pipeline.

Method and system for improving detail information in digital images

An image processing apparatus includes a direction detection unit configured to detect a direction of a picture pattern of each picture in an input image having a predetermined color pixel array; a moire detection unit configured to detect a moire level by periodicity of a pixel value of each pixel in the input image depending on the direction as a moire index value; and a moire suppression unit configured to suppress the moire level in the input image based on the moire index value detected by the moire detection unit. A method thereof and a program therefor are also provided.

Image processing apparatus, method thereof and program therefor

A method for effectively performing local image similarity measurement is proposed. A system equipped with such a method for effectively performing an image processing task includes an image processor that performs an intermediate-results calculation procedure to calculate intermediate result values that are based upon corresponding pixels of a target patch and one or more similar patches. The image processor typically moves the target patch of the intermediate-results calculation to different locations in a raster order or some other organized order. The image processor then performs an intermediate-results combination procedure by calculating appropriate statistics of the intermediate result values to produce processed pixel values. A processor device typically controls the image processor to effectively perform the image processing tasks including, but not limited to, demosaicing and denoising.

Method to measure local image similarity and its application in image processing

An image processing method includes the steps of calculating, with respect to a processing-target pixel in an input image signal, a concentric aberration correction amount for concentric aberration that is a component of magnification chromatic aberration, the concentric aberration causing a color shift to occur in a concentric manner from the center of an image, calculating, with respect to the processing-target pixel, a uniform aberration correction amount for uniform aberration that is a component of magnification chromatic aberration, the uniform aberration causing a color shift direction and a color shift amount to uniformly occur on a whole image, and correcting a pixel value of the processing-target pixel on the basis of the calculated concentric aberration correction amount and the calculated uniform aberration correction amount.

Image processing device, image processing method, and program

A system and method for effectively performing wavelet transforms on incomplete image data includes an image processor that performs a green-pixel transformation procedure on incomplete color pixel matrices. The image processor then rearranges red, blue and transformed green-pixel into four quadrants of contiguous pixels and applies some two dimensional (2D) wavelet thresholding schemes on each quadrant. After thresholding, an inverse procedure is applied to reconstruct the pixel values on the incomplete color pixel matrices. For further de-correlation of image data, the image processor may stack similar image patches in a three dimensional (3D) array and apply incomplete-data wavelet thresholding on the 3D array. The incomplete-data wavelet thresholding procedure may be put in an improved local similarity measurement framework to achieve better performance of image processing tasks. A CPU device typically controls the image processor to effectively perform the image processing procedure.

Wavelet transform on incomplete image data and its applications in image processing

An image denoising system and method of implementing the image denoising system is described herein. Noise is decomposed within each channel into frequency bands, and sub-band noise is propagated. Denoising is then able to occur at any node in a camera pipeline after accurately predicting noise that is signal level-dependent, frequency dependent and has inter-channel correlation. A methodology is included for estimating image noise in each color channel at a sensor output based on average image level and camera noise parameters. A scheme is implemented for detecting a peak-white image level for each color channel and predicting image level values for representative colors. Based on a noise model and camera parameters, noise levels are predicted for each color channel for each color patch and these noise levels are propagated to the denoising node. A three dimentional LUT correlates signal level to noise level. Then, a denoising threshold is adaptively controlled.

Method of and apparatus for image denoising

An imaging apparatus includes: a shift lens driven to be shifted; a uniform aberration correction data storing unit storing uniform aberration correction data for correcting uniform aberration; a shift amount calculating unit calculating an amount of aberration correction shift of the shift lens for correcting uniform aberration using the uniform aberration correction data; a shift lens driving unit driving the shift lens to be shifted according to an amount of driving shift based on the amount of aberration correction shift; a concentric aberration correction amount calculating unit calculating an amount of concentric aberration correction for correcting concentric aberration with respect to a process target pixel of an input image signal; and a pixel value correcting unit correcting a pixel value of the process target pixel on the basis of the calculated amount of concentric aberration correction.

Imaging apparatus, aberration correcting method, and program

A method and apparatus for performing a blur rendering process on an image is disclosed. In one embodiment, the method of performing a blur rendering process includes accessing a filtered image and depth map information, determining a plurality of blending coefficients for computing a weighted sum for the image and filtered image, wherein the plurality of blending coefficients define a substantially smooth transition from at least one first depth class to at least one second depth class and a substantially sharp transition from the at least one second depth class and the at least one first depth class, wherein the at least one first depth class and the at least one second depth class form at least a portion of a plurality of depth classes and combining the image and the filtered image into a resulting image using the plurality of coefficients.

Method and apparatus for performing a blur rendering process on an image

An aberration correction apparatus includes: a correction data holding section that holds correction data on a correction level used to correct chromatic aberration of magnification; a center position discrepancy data holding section that holds center position discrepancy data on the discrepancy between the center of an image area and the position of an aberration center of the chromatic aberration of magnification; an aberration center calculating section that selects one of the center position discrepancy data that corresponds to a combination of the discrepancy variation conditions in an imaging apparatus and calculates the position of the aberration center based on the selected center position discrepancy data; an image height calculating section; a magnification chromatic aberration correction level calculating section; and a pixel value correcting section.

Aberration correction apparatus, aberration correction method, and program

A fast accurate multi-channel frequency dependent scheme for analyzing noise in a signal processing system is described herein. Noise is decomposed within each channel into frequency bands and sub-band noise is propagated. To avoid the computational complexity of a convolution, traditional methods either assume the noise to be white, at any point in the signal processing pipeline, or they just ignore spatial operations. By assuming the noise to be white within each frequency band, it is possible to propagate any type of noise (white, colored, Gaussian, non-Gaussian and others) across a spatial transformation in a very fast and accurate manner. To demonstrate the efficacy of this technique, noise propagation is considered across various spatial operations in an image processing pipeline. Furthermore, the computational complexity is a very small fraction of the computational cost of propagating an image through a signal processing system.

Method of and apparatus for analyzing noise in a signal processing system

An image processing apparatus to perform a denoising process of removing noise included in image data based on a predetermined parameter is provided. The image processing apparatus includes an input unit configured to input image data; a physical characteristic value calculating unit configured to calculate a brightness-dependent physical characteristic value based on brightness of the input image data; a color determining unit configured to determine a color of the input image data; and a parameter deciding unit configured to decide the parameter based on the physical characteristic value calculated by the physical characteristic value calculating unit and a visual characteristic value according to the color determined by the color determining unit.

Image processing apparatus with noise reduction capabilities and a method for removing noise from a captured image

A system for and method of calibrating an imaging device efficiently is described herein. The imaging device acquires an image of an object that is more than one color. The information acquired is then transferred to a computing device. The information is then used to generate a set of data which represents information which was not acquired in the image. The set of data is generated based on statistical prediction using a training data set. Using acquired image information and the set of data, an imaging device is able to be calibrated. Since the process of calibration utilizing this method only requires one image to be acquired and a reduced set of image information to be sent to the computing device, the process is more efficient than previous implementations.

Sequential regression for calibration from residues

A system for and method of determining calibration parameters while only capturing a single image is described herein. Furthermore, traditional calibration algorithms are avoided by the direct determination of the calibration parameters. The determination of the calibration parameters is possible by first determining a training data set from images acquired of a variety of objects with a multitude of colors. Then, using the training data set, regression coefficients are generated. A camera to be calibrated then acquires only one set of image information such as a single picture. Then, using the regression coefficients and the acquired information, the calibration parameters are directly estimated for that camera.

Direct calibration of color imaging devices

A method and system are provided for approximating spectral sensitivities of a particular image sensor, the image sensor having a color filter array positioned over the image sensor. In one example of the method, the method involves measuring spectral sensitivities of a set of image sensors each having a color filter array positioned over the image sensor, calculating mean spectral sensitivities of the set of image sensors for each color within the color filter array, measuring outputs of a particular image sensor when capturing a picture of a plurality of color patches under a first illuminant and calculating spectral sensitivities of the particular image sensor using the mean spectral sensitivities and the output of the particular image sensor. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to determine outputs of the particular image sensor under a second illuminant. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to calibrate a camera including the image sensor.

Method for device spectral sensitivity reconstruction

A system and method for effectively performing an image data transformation procedure may include an electronic camera device that is implemented to capture primary image data corresponding to a photographic target. A transformation manager in the electronic camera device may be configured to convert the primary image data into secondary image data by utilizing selectable transformation parameters that are optimized by utilizing an optimization metric to thereby minimize noise characteristics in the secondary image data. The transformation parameters may be stored in parameter lookup tables in the electronic camera device for use by the transformation manager in performing the image data transformation procedure.

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Kanagawa, Japan

Kenichi Nishio