Realtek Semiconductor Inc.

Tsung-Hsuan Li

Shih-Tse Chen

Wan-Ju Tang

Hao-Tien Chiang

Yang-Ting Chou

Shang-Lun Chan

Chao-Hsun Yang

Kai-Chun Su

Shian-shin Lu

Yu-Hsuan Kuan

A method for determining deblur filter coefficients includes the following steps: generating an edge profile according to the data of a blurred image; estimating a blur kernel according to the edge profile, wherein the blur kernel indicates how an imaging process blurs original image data and thereby generates blurred image data; and determining coefficients of a deblur filter to make a process result of the deblur filter processing the blurred image data approach the original image data.

Method for determining coefficients of deblur filter

The present invention discloses an image data augmentation method that includes the steps outlined below. At least one distortion operation function is retrieved. A plurality of pixels included in the image are twisted according to the distortion operation function to generate at least one augmented image. Object information of each of at least one object included in the image is converted according to the distortion operation function to generate object information conversion result. The augmented image, a class tag of each of the at least one object and the object information conversion result are fed to a machine learning module to generate a machine learning result.

Image data augmentation apparatus and method

An image processing method includes: receiving an input image; performing a low-frequency image regulating operation to regulate the local intensity of the image of pixel unit(s) according to low-frequency information of the image of pixel unit(s) of the input image; performing a high-frequency image regulating operation to improve the details of the image of pixel unit(s) according to high-frequency information of the image of pixel unit (s) of the input image; and, generating an output image according to the input image, the low-frequency image regulating operation, and the high-frequency image regulating operation.

Image processing apparatus and image processing method for contrast enhancement

A method for image compression and a circuit system thereof are provided. In the method, pixel values of an image are obtained. A compression scenario is decided, for example, a uniform-quantization manner or a non-uniform-quantization manner is used for an M-bit image being compressed to an N-bit image so as to decide codeword sections for the image. Every codeword section has a codeword distance. The codeword sections have a fixed codeword distance in the uniform-quantization manner. Alternatively, in the non-uniform-quantization manner, the image can be divided into multiple codeword sections having different codeword distances according to a brightness distribution. Afterwards, a random number is generated for deciding codeword and index for original value of each of the pixels. An index table is accordingly formed. The index table is provided for obtaining the codeword in a decoding process by querying a codebook with the index.

Method for image compression and circuit system thereof

A method and a system for image correction are provided. The system includes an image processor that performs the method for image correction when receiving an input image. In the method, an overexposed area within the image is determined. The channel values of pixels in the overexposed area are applied with corresponding weights. An automatic white balance process is performed on the channel values of pixels in the overexposed area according to an expectation of low saturation in the overexposed area. Besides a first channel value of each of the pixels, a second channel value and a third channel value of the pixel can be estimated according to the channel values of periphery pixels. A correction value overexposed of the first channel value of the current pixel can be calculated according to the corresponding weights of the pixels in the overexposed area.

Method and system for image correction

A circuitry for image demosaicing and contrast enhancement and an image-processing method thereof are provided. The circuitry includes a storage device that is used to temporarily store an image and is jointly used by circuits that perform color restoration and brightness reconstruction. The circuitry includes a color restoration circuit for performing image interpolation and a global mapping circuit that performs mapping to obtain brightness of an image according to restored red, green and blue information of every pixel. Further, an edge texture feature decision circuit is provided to obtain each pixel's directionality for color restoration. A brightness estimation circuit utilizes green information of the pixels as the brightness for an area. After that, a color image with the color restoration and brightness reconstruction is outputted.

Circuitry for image demosaicing and contrast enhancement and image-processing method

A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

Pixel value calibration method and pixel value calibration device

Disclosed is an image processing method based on sensor characteristics. The method includes the following steps: obtaining a constant of a relation 'c&#x2248;&#x3c3;(x)/&#x221a;{square root over (x)}' between output signals 'x' and the noise standard deviation &#x201c;&#x3c3;(x)&#x201d; of the output signals, in which the output signals are outputted by a sensor based on a sensor gain such as an ISO value of a camera; and calculating an output value of a target pixel according to pixel values within a sampling window, the constant, and a front-end gain such as an auto exposure gain, in which the pixel values include an input value of the target pixel.

Image processing method based on sensor characteristics

A method and a system for pixel channel imbalance compensation for an image sensing circuit are provided. The system includes an image acquisition circuit having a lens, a color filter and an image sensor and a processing circuit. In the method performed by the processing circuit, a second frame image is retrieved from a motion image, and a first frame image that has undergone noise reduction can be retrieved from a memory. Motion detection is performed between the frames by comparing the first frame image and the second frame image. The motion detection is referred to as a reference for determining how to perform 3D noise reduction. A compensation value for channel imbalance between the adjacent channels can be estimated based on the image under noise reduction in a same buffer. While the pixel channel imbalance is compensated, the image is then restored by an interpolation method.

Method and system for pixel channel imbalance compensation for image sensing circuit

Disclosed are a color reconstruction device and method capable of accurately recovering the missing color of a target pixel. The device includes: a direction-characteristic estimation circuit calculating a horizontal-variation characteristic and a vertical-variation characteristic according to a first color of a target pixel and the values of pixels within a reference range, in which the target pixel is in the reference range and a current value of the target pixel is a first color value; an edge-texture decision circuit determining which of N predetermined relations matches the relation between the horizontal-variation characteristic and the vertical-variation characteristic and thereby determining the directional characteristic of the target pixel, in which the N is not less than four; and a color recovery circuit calculating a second and a third color values of the target pixel according to the directional characteristic and the values of the pixels within the reference range.

Color reconstruction device and method

A denoising method based on a signal-to-noise ratio (SNR), which includes: obtaining a current input coefficient; obtaining a current noise standard deviation by querying a first relationship table; querying a second relationship table according to the current noise standard deviation and the current input coefficient to obtain a current slope corresponding to the current input coefficient; generating a current output coefficient by multiplying the current input coefficient and a compression magnification function; and calculating the current output coefficient by substituting the current noise standard deviation, the current input coefficient, and the current slope.

Denoising method based on signal-to-noise ratio

A method of updating background model for an image processing device is disclosed. The method includes receiving at least a background image, counting a number of occurrences of a color of each pixel of the background image according to a color information of each pixel of the at least a background image, to establish a background model including a statistical color information about the color and the number of occurrences of the color, receiving a current image, and determining whether to update the number of occurrences of the color in the background model corresponding to a color of each pixel of the current image according to a color information of each pixel of the current image and a result of comparing a random number with a threshold.

Method of updating background model and related device

Disclosed is a method for determining filter coefficients. The method includes: obtaining the coefficients of a target filter and calculating the response of the target filter; computing according to collected data and/or predetermined data in accordance with a first data pattern so as to have the response of a first filter approximate to the response of the target filter and thereby determine the coefficients of the first filter; and computing according to the collected data and/or the predetermined data in accordance with a second data pattern so as to have the response of a second filter approximate to the response of the target filter and thereby determine the coefficients of the second filter. Accordingly, the difference between the responses of the first filter and the second filter is insignificant and results in less negative influence; and the first and the second filters can replace the target filter to reduce cost.

Method for determining filter coefficients

The present invention discloses a false color removal method comprising: receiving a current pixel including a first color value, a second color value and a third color value, in which the three color values are composed of a maximum value, a medium value and a minimum value; performing permutation to the maximum value, the medium value and the minimum value to obtain six permutation results; calculating six weighting values, in which a kweighting value is calculated according to at least a kpermutation result of the six permutation results and the three color values, and the k stands for an integer-variable between one and six; calculating six products, in which a kproduct is obtained by multiplying the kweighting value by the kpermutation result; and using the sum of the six products to update the first, second and third color values.

False color removal method

An image-filtering method that includes the steps outlined below is provided. Target image values and an input image having input pixel values are retrieved. A difference function between filtering response values of a desired output image and the target image values is determined, wherein the filtering response values are generated by filtering desired output pixel values of the desired output image based on characteristic filtering coefficients. An optimal solution of a desired output central pixel value of the desired output image is calculated according to a linear equation related to the characteristic filtering coefficients, wherein the optimal solution minimizes a value of the difference function. A corresponding relation between the desired output central pixel values and the input pixel values are retrieved from the optimal solution to calculate optimal filtering coefficients. A filtering circuit performs filtering on each pixel of an image under processing according to the optimal filtering coefficients.

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Taichung, Taiwan

Tsung-Hsuan Li