Abstract: A color correction method is applied to an image correction apparatus having an image sensor, and includes searching a color deviation area within a detection image, analyzing the detection image to estimate a correction color value of the color deviation area, and calibrating the color deviation area by the correction color value to generate a calibrated detection image without color deviation.

Abstract: A deep neural network (DNN) processing device with a decompressing module, comprises a storage module, for storing a plurality of binary codes, a coding tree, a zero-point value and a scale; a decompressing module, coupled to the storage module, for generating a quantized weight array according to the plurality of binary codes, the coding tree and the zero-point value wherein the quantized weight array is generated according to an aligned quantized weight array and the zero-point value; and a DNN processing module, coupled to the decompressing module, for processing an input signal according to the quantized weight array and the scale.

Abstract: A color correction method is applied to an image correction apparatus having an image sensor, and includes searching a color deviation area within a detection image, analyzing the detection image to estimate a correction color value of the color deviation area, and calibrating the color deviation area by the correction color value to generate a calibrated detection image without color deviation.

Abstract: An image calibration method applied to a wide-angle image and executed by an image calibration apparatus includes applying primary lens distortion correction for the wide-angle image to generate a corrected image, segmenting an foreground image from the corrected image to generate a background image, applying secondary distortion correction for the foreground image based on the pre-defined object to generate a calibrated foreground image, fusing the background image with the calibrated foreground image to generate a fused image, detecting at least one residual empty pixel not overlapped by the calibrated foreground image within the fused image, and utilizing a machine learning algorithm to fill the at least one residual empty pixel of the fused image by extending the background image to provide an output image. The foreground image contains feature pixels relate to a pre-defined object and the background image has empty pixels corresponding to the foreground image.

Abstract: A transmission interface module includes a receiving unit, a transmitting unit, a multiplexer, and a processing unit. An output terminal and a control terminal of the receiving unit are electrically connected to the processing unit. The input terminal and the control terminal of the transmitting unit are electrically connected to the processing unit, and the control terminal of the multiplexer is electrically connected to the processing unit. The transmission interface module respectively adjusts a turn-on state or a turn-off state of the analog power terminal, the digital power terminal, the processing unit, the receiving unit, the transmitting unit, and the multiplexer through a plurality of operation modes to transmit the detecting signals.

Abstract: An image capture device, a depth generating device and a method thereof are disclosed. The present disclosure is characterized in that a depth calculation technology with a structure light projection and a pictorial depth calculation technology are combined to better both of resolution and accuracy of the calculated image depth. In addition, the utilization of a modified flashlight enables the combination of the two technologies to be applied to a hand-held capture device.

Abstract: Disclosure is an image synchronization method and system thereof being applied to a first image detection unit and a second image detection unit. The system comprises a monitoring unit, an extension interval generation unit and a control unit. The monitoring unit monitors the first image detection unit and the second image detection unit. When a time difference between output time of second image data and output time of first image data is larger than or equal to a postponed time threshold, a synchronization signal is transmitted by the monitoring unit. The extension interval generation unit bases the time difference and the rate of outputting the first image to generate an extension interval. When receiving the synchronization signal, the control unit adjusts the rate of outputting the first image by controlling the first image detection unit. Therefore, the first image data and the second image data could be synchronized.

Abstract: An image processing system and a method for object-tracing are provided. The method includes: receiving a first wide field of view image and a first narrow field of view image, and determining a to-be-traced object therefrom and choosing one image therefrom for serving as a first output image; using an area size of the to-be-traced object in the first output image as a reference area; comparing the reference area with the area sizes of the to-be-traced object from a second wide field of view image and a second narrow field of view image respectively so as to determine one of that as a main image, and respectively zooming and deforming the main image and an area corresponding to the other image, and then fusing a zoomed and deformed second image as a second output image.

Abstract: The present invention discloses a method of capturing images and an image capturing device using the method. The image capturing device has a first lens module and a second lens module having a view angle which is smaller than that of the first lens module. The method comprises: increasing an exposure time and reducing a light sensitivity value of the first lens module and capturing a first image by using the first lens module; reducing the exposure time and increasing the light sensitivity value of the first lens module and capturing a second image by using the second lens module; extracting a plurality of image features from the first image and the second image respectively and determining a region corresponding to the second image in the first image; and merging the second image into the region in the first image to generate an output image.

Abstract: An image capturing device and a digital zoom display method are provided. The device includes two lens modules configured to respectively capture a first image and a second image, and their capturing view fields are substantially identical, a preview processing unit configured to generate a third image by down-sampling the first image according to a display unit's resolution, a feature extraction unit, an image zooming and distortion unit and an image fusing unit. The second image is trimmed for generating a fourth image according to the same size of the third image. A pixel offset feature corresponding with the third and fourth images is generated by the feature extraction unit. The image zooming and distortion unit generates a fifth image and a sixth image according to the pixel offset feature and zoom ratio to the image fusing unit to generate a combined image displayed on the display unit.

Abstract: The present disclosure illustrates a lens distortion correction method to solve influence for distortion correction caused by an alignment error between a lens center and a center of image sensor unit during assembling. The present disclosure is characterized in that the spatial geometric calibration is incorporated with the lens distortion correction and different image centers are selected repeatedly when transformation relationship of image coordinates is used to perform the lens distortion correction, so as to correct the image center of the image to be corrected and enhance the accuracy of lens distortion correction.

Abstract: The present disclosure illustrates a lens distortion correction method to solve influence for distortion correction caused by an alignment error between a lens center and a center of image sensor unit during assembling. The present disclosure is characterized in that the spatial geometric calibration is incorporated with the lens distortion correction and different image centers are selected repeatedly when transformation relationship of image coordinates id used to perform the lens distortion correction, so as to correct the image center of the image to be corrected and enhance the accuracy of lens distortion correction.

Abstract: An image capture device, a depth generating device and a method thereof are disclosed. The present disclosure is characterized in that a depth calculation technology with a structure light projection and a pictorial depth calculation technology are combined to better both of resolution and accuracy of the calculated image depth. In addition, the utilization of a modified flashlight enables the combination of the two technologies to be applied to a hand-held capture device.

Abstract: The present disclosure illustrates an image adjusting system with multiple lens modules and method thereof. The system is adapted for an image capturing device having a zoom lens module and a prime lens module. When the zoom lens module and the prime lens module captures a first image and a second image, the image adjusting system can select a first reference calibration parameter table from multiple first calibration parameter tables, and search a current focus parameter from a focus parameter lookup table according to a current motor step, and then multiply the second image, the first reference calibration parameter table and the current focus parameter to generate a third image. Therefore, the third image has substantially consistent with the first image.

Abstract: The present disclosure illustrates a camera array correction method. The method comprises steps of calculating the gain values between the images captured by cameras with the pre-measured characteristic of each of the cameras. By such correction method, the different cameras in the camera array can capture images of the same brightness values and color values simultaneously. In addition, the huge computation requirement due to the increasing of the cameras can be reduced effectively.

Abstract: The present disclosure illustrates an auto-focus system using multiple lenses and method thereof, the system is characterized in using multiple lenses and combining multiple focus approaches to improve focus speed and precision. In particular, the system moves multiple lenses simultaneously to perform focusing for reducing focus time efficiently.

Abstract: The present disclosure illustrates an auto-focus system using multiple lenses and method thereof. The system is characterized in using multiple lenses and combining a focus depth based approach and a contrast value based approach to improve focus speed and precision. In particular, the system can avoid the impairment caused by the repeat pattern in the focus depth based approach.

Abstract: A method of detecting the subject of an image and imaging device thereof are disclosed. The method comprises the following steps of providing an image capturing module to capture the temporal image, providing the image processing module to receive the temporal image and to determine a plurality of objects from the temporal image, using the image processing module to define a plurality of regions on the temporal image based on the center of temporal image, using the image processing module to assign an initial setup value to each of the objects according to the region corresponding to each of the objects, using the imaging processing module to execute an operation to increase or decrease each of the initial setup values, using the image processing module to execute a sorting, and selecting at least one of the objects as a subject of the temporal image.

Abstract: The present invention discloses a method of image denoising and method of generating motion vector data structure thereof. The method comprises: providing an image sequential capturing module to capture and to receive the plurality of images; generating a global motion vector based on the plurality of images in accordance with a first algorithm; reducing each image as reduced images; dividing each of the first reduced images into a plurality of first areas and generating a first local motion vector based on each of the first areas in accordance with a second algorithm, and via the similar way for generating a second local motion vector; finally, obtaining motion vector data in the plurality of images according to the global motion vector, each of the first local motion vectors and each of the second local motion vectors.

Abstract: The present invention discloses a method of image denoising and method of generating motion vector data structure thereof. The method comprises: providing an image sequential capturing module to capture and to receive the plurality of images; generating a global motion vector based on the plurality of images in accordance with a first algorithm; reducing each image as reduced images; dividing each of the first reduced images into a plurality of first areas and generating a first local motion vector based on each of the first areas in accordance with a second algorithm, and via the similar way for generating a second local motion vector; finally, obtaining motion vector data in the plurality of images according to the global motion vector, each of the first local motion vectors and each of the second local motion vectors.