Abstract:
This invention discloses an image processing device and an image processing method. The image processing device includes a line buffer, a pixel enhancing module, a smoothing module, a noise reduction module and a contrast adjusting module. The line buffer stores a plurality of pixel values of an image. The pixel enhancing module performs an edge-enhancing operation on the image. The smoothing module filters the image to improve the image in terms of roughness. The noise reduction module filters the image to improve the image in terms of a signal-to-noise ratio. The contrast adjusting module checks whether a target pixel is on a thin edge to decide the method of adjusting the contrast of the image.
Abstract:
Disclosed is an image processing method based on sensor characteristics. The method includes the following steps: obtaining a constant of a relation “c≈σ(x)/√{square root over (x)}” between output signals “x” and the noise standard deviation “σ(x)” 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.
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:
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 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.
Abstract:
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.
Abstract:
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.
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:
A white balance adjusting method with scene detection comprises: receiving source image data corresponding to a plurality of sensing units; generating an initial white balance gain according to the source image data; generating an image parameter corresponding to a plurality of parameter elements according to the source image data; determining whether a preset condition is satisfied according to the image parameter to generate a decision result; determining whether the source image data are associated with any of built-in scenes according to the decision result in which the built-in scenes include a first scene in connection with a first-scene adjustment rule; and if the source image data are associated with the first scene, adjusting the initial white balance gain or processing the source image data according to the first-scene adjustment rule to thereby obtain a scenic white balance gain and adjust the source image data with it.