Abstract: A circuitry for image demosaicing and 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.

Abstract: 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.

Abstract: 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 kth weighting value is calculated according to at least a kth permutation 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 kth product is obtained by multiplying the kth weighting value by the kth permutation result; and using the sum of the six products to update the first, second and third color values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 kth weighting value is calculated according to at least a kth permutation 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 kth product is obtained by multiplying the kth weighting value by the kth permutation result; and using the sum of the six products to update the first, second and third color values.

Abstract: An image contrast enhancement method and an apparatus thereof are disclosed, which calculate the degree of influencing the clarity according to the influence feature (e.g., heavy fog, dust, smoke, or etc.) in the image, and then adjust the brightness of the pixels corresponding to features of influencing the clarity according to the degree, thereby enhancing image contrast and removing phenomenon of influencing the clarity in the image.

Abstract: An image-filtering device for filtering an image that includes a pixel difference computing module, an adaptive brightness adjusting module, a weighting computing module and a filter computing module is provided. The pixel difference computing module uses any one of the pixels as a central pixel within a pixel window to compute pixel absolute differences between the central pixel and every pixels within the pixel window. The adaptive brightness adjusting module multiplies each of the pixel absolute differences with an adjusting parameter to generate adjusted pixel absolute differences. The weighting computing module generates weighting values according to the adjusted pixel absolute differences. The filter computing module performs convolution according to the pixel value of each of the pixels within the pixel window and the corresponding weighting values to generate a filtering result of the central pixel.

Abstract: Provided is a fast divider including an initial parameter setting unit and an arithmetic unit. The arithmetic unit is coupled to the initial parameter setting unit that receives a divisor and a dividend, and sets a plurality of initial parameters of a sequence according to the divisor and the dividend. The plurality of initial parameters includes an initial term, a first term and a common ratio having an absolute value smaller than 1. The arithmetic unit stores a recurrence relation of the sequence and iteratively computes a quotient using the recurrence relation according to the plurality of initial parameters. The recurrence relation indicates that a (k+1)th term is equal to a product of a kth term multiplied by a sum of the common ratio and 1 subtracted by a product of a (k?1)th term multiplied by the common ratio. k is an integer larger than or equal to 1.

Abstract: An image de-noising method and an apparatus thereof are disclosed, which includes categorizing a pixel in a current frame into a first low-frequency pixel having a first weight and a first high-frequency pixel having a second weight; categorizing a previous pixel corresponding to the position of the pixel in a previous frame into a second low-frequency pixel having a third weight and a second high-frequency pixel having a fourth weight; adjusting the first weight and the third weight and calculating the weighted sum of the first low-frequency pixel and the second low-frequency pixel, to generate low-frequency pixel data; adjusting the second weight and the fourth weight and calculating the weighted sum of the first high-frequency pixel and the second high-frequency pixel, to generate high-frequency pixel data; and calculating the sum of the low-frequency pixel data and the high-frequency pixel data, to output the de-noised pixel.

Abstract: 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.

Abstract: An image contrast enhancement method and an apparatus thereof are disclosed, which calculate the degree of influencing the clarity according to the influence feature (e.g., heavy fog, dust, smoke, or etc.) in the image, and then adjust the brightness of the pixels corresponding to features of influencing the clarity according to the degree, thereby enhancing image contrast and removing phenomenon of influencing the clarity in the image.

Abstract: An image-filtering device for filtering an image that includes a pixel difference computing module, an adaptive brightness adjusting module, a weighting computing module and a filter computing module is provided. The pixel difference computing module uses any one of the pixels as a central pixel within a pixel window to compute pixel absolute differences between the central pixel and every pixels within the pixel window. The adaptive brightness adjusting module multiplies each of the pixel absolute differences with an adjusting parameter to generate adjusted pixel absolute differences. The weighting computing module generates weighting values according to the adjusted pixel absolute differences. The filter computing module performs convolution according to the pixel value of each of the pixels within the pixel window and the corresponding weighting values to generate a filtering result of the central pixel.

Abstract: An image de-noising method and an apparatus thereof are disclosed, which includes categorizing a pixel in a current frame into a first low-frequency pixel having a first weight and a first high-frequency pixel having a second weight; categorizing a previous pixel corresponding to the position of the pixel in a previous frame into a second low-frequency pixel having a third weight and a second high-frequency pixel having a fourth weight; adjusting the first weight and the third weight and calculating the weighted sum of the first low-frequency pixel and the second low-frequency pixel, to generate low-frequency pixel data; adjusting the second weight and the fourth weight and calculating the weighted sum of the first high-frequency pixel and the second high-frequency pixel, to generate high-frequency pixel data; and calculating the sum of the low-frequency pixel data and the high-frequency pixel data, to output the de-noised pixel.

Abstract: Provided is a fast divider including an initial parameter setting unit and an arithmetic unit. The arithmetic unit is coupled to the initial parameter setting unit that receives a divisor and a dividend, and sets a plurality of initial parameters of a sequence according to the divisor and the dividend. The plurality of initial parameters includes an initial term, a first term and a common ratio having an absolute value smaller than 1. The arithmetic unit stores a recurrence relation of the sequence and iteratively computes a quotient using the recurrence relation according to the plurality of initial parameters. The recurrence relation indicates that a (k+1)th term is equal to a product of a kth term multiplied by a sum of the common ratio and 1 subtracted by a product of a (k?1)th term multiplied by the common ratio. k is an integer larger than or equal to 1.