Abstract: An image processing method includes the following steps. Firstly, a block-size value is obtained. Then, a first frame into a plurality of first blocks according to the block-size value is divided. Then, a second frame into a plurality of second blocks according to the block-size value is divided. Then, a noise reduction intensity array is obtained according to a first pixel information of each first block and a second pixel information of each second block. Then, an output frame is obtained according to the noise reduction intensity array, the first frame and the second frame.

Abstract: A hardware accelerator is provided. The hardware accelerator includes a first memory; a source address generation unit coupled to the first memory; a data collection unit coupled to the first memory; a first data queue coupled to the data collection unit; a data dispersion unit coupled to the first data queue; a destination address generation unit coupled to the data dispersion unit; an address queue coupled to the destination address generation unit; a second data queue coupled to the data dispersion unit; and a second memory coupled to the second data queue. The hardware accelerator can perform anyone or any combination of tensor stride, tensor reshape and tensor transpose to achieve tensorflow depth-to-space permutation or tensorflow space-to-depth permutation.

Abstract: The present invention discloses an image processing method. The image processing method includes the following steps: (a), a to-be-processed image is corrected as a first correction image according to a first mapping relationship along a correction direction; (b) the first correction image by an angle is rotated; and (c) the rotated first correction image is corrected as a second correction image according to a second mapping relationship along the same correction direction. In embodiment, given that the to-be-processed image is deformed along two different directions, the to-be-processed image is corrected along the same correction direction, such that correction complexity could be reduced.

Abstract: An encryption and signature device for AI model protection is provided. The encryption and signature device for AI model protection includes a key derivation unit, a model encryption unit, a model password encryption unit, an image generation unit and a signature unit. The key derivation unit is configured to derive a model key according to a model password and a derivation function. The model encryption unit is configured to encrypt an AI model according to the model key to generate an encrypted AI model. The model password encryption unit is configured to encrypt the model password to generate an encrypted model password. The image generation unit is configured to generate an image file according to the encrypted model password and the encrypted AI model. The signature unit is configured to sign the image file according to a private key to obtain a signed image file.

Abstract: An image processing method includes: (a) receiving a to-be-processed image; (b) obtaining a target brightness of the to-be-processed image; (c) creating a histogram curve of the to-be-processed image; (d) clipping the histogram curve according to a clipping value; (e) obtaining a mapping function of the clipped histogram curve; (f) obtaining a mapped image mapped according to the mapping function; (g) obtaining an average brightness of the mapped image; (h) obtaining a difference between the target brightness and the average brightness; and (i) if the difference is greater than a threshold, adjusting the clipping value and repeating steps (d) to (i) until the difference is less than the threshold. Thus, if the to-be-processed image does not reach the target brightness, the image processing device automatically repeats steps (c) to (i) until the to-be-processed image reaches the target brightness.

Abstract: A transmitter is provided. the transmitter includes a hybrid feedback circuit and a hybrid driving circuit. The hybrid feedback circuit compares a reference voltage with a feedback voltage in closed-loop, determines whether to perform polarity reversal according to a mode control signal, controls power output according to a comparison result and the mode control signal, and generates a first output signal. The hybrid driving circuit, coupled to the hybrid feedback circuit, receives the first output signal of the hybrid feedback circuit, generates a transmitter output signal according to an input data, and generates a second output signal according to the transmitter output signal. The first output signal and the second output signal are transmitted back to the hybrid feedback circuit.