Samsung Electronics Co., Ltd.

Evgeny Artyomov

German Voronov

Eugene Fainstain

Jong-Hwa Lee

Ju Young Lee

Roee Sfaradi

Michael Dinerstein

Roy Josef Jevnisek

Artem Zinevich

Niv Zehngut

Or Shimshi

Amir Ben-dror

Shlomo Somerstein

Methods, systems, and apparatus for combined or separate implementation of coarse-to-fine neural architecture search (NAS), two-phase block NAS, variable hardware prediction, and differential hardware design are provided and described. A variable predictor is trained, as described herein. Then, a controller or policy may be used to iteratively modify a neural network architecture along dimensions formed by neural network architecture parameters. The modification is applied to blocks (e.g., subnetworks) within the neural network architecture. In each iteration, the remainder of the neural network architecture parameters are modified and learned with a differential NAS method. The training process is performed with two-phase block NAS and incorporates a variable hardware predictor to predict power, performance, and area (PPA) parameters. The hardware parameters may be learned as well using the variable hardware predictor.

Method for co-design of hardware and neural network architectures using coarse-to-fine search, two-phased block distillation and neural hardware predictor

In one example embodiment, a method includes determining at least one of integral column sums and integral row sums for pixels of an image and determining at least one of a column-wise sum of pixel values and a row-wise sum of pixel values associated with an area within the image based on at least one of the determined integral column sums and the determined integral row sums corresponding to a plurality of the pixels forming the area.

Methods and systems for object detection based on column-wise and row-wise sums of pixel values

An image processing method includes: receiving image data of a Bayer format comprising red color information, green color information, and blue color information, generating image data of a modified Bayer format by combining the green color information with the red color information and combining the green color information with the blue color information while downscaling the image data of the Bayer format to a target resolution, denoising the image data of the modified Bayer format, and generating RGB image data by demosaicing the denoised image data of the modified Bayer format.

Image processing method, image signal processor, and image processing system including the same

A noise elimination device includes a low pass filter unit, a noise filtering unit and a signal synthesis unit. The low pass filter unit generates a low frequency image signal by separating low frequency elements from an input image signal. The noise filtering unit generates a filtered image signal by suppressing noise in the low frequency image signal in horizontal and vertical directions. The signal synthesis unit generates an output image signal by combining the high frequency image signal and the filtered image signal.

Noise suppression devices and image capturing apparatuses having the same

A denoising apparatus comprising an image input unit which receives pixel data including color information of pixels included in a correction target image, a denoising unit which denoises the pixel data by a weight based averaging method, wherein the weight is set to a maximum value when a difference value between a correction target block and a comparison target block in the correction target image is zero, decreases linearly to zero as the difference value increases until it reaches a threshold value, and is set to zero when the difference value is greater than or equal to the threshold value, and an image output unit which outputs the pixel data processed by the denoising unit. The denoising unit assigns a corrected weight value to at least a guaranteed number of comparison target blocks for an impulse block, where an impulse block is a correction target block for which the number of non-zero weight valued comparison target blocks is less than a predetermined guaranteed number.

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Rehovot, Israel

Evgeny Artyomov