Image processing apparatus and image processing method are provided. The image processing apparatus may include an image sensor having a plurality of photodetectors and include a 3D image calculating module. The image sensor may be configured to generate a first set of input information at a first time/first location and a second set of input information at a second time/second location, where the first set of input information may be associated with a first weighting value, and the second set of input information may be associated with a second weighting value. The 3D image calculating module may be configured to generate output information based on the first and the second sets of input information and the first and the second weighting values, wherein at least one of the plurality of photodetectors includes germanium.

The method for processing a matrix of pixels each containing an original red, green, blue, or infrared component, comprises at least one interpolation of an interpolated component different from the original component of a pixel of interest from the components of a group of pixels neighboring the pixel of interest. The interpolation comprises: a calculation of the sum of the components of reference pixels weighted by a respectively assigned weight, the reference pixels being pixels of the group having the same original component as the interpolated component, an evaluation of the spatial uniformity of an environment, within the group of each reference pixel, a calculation of the weights assigned to the reference pixels at values which are normalized and proportional to the respective spatial uniformity.

A device comprising an image processor, the image processor being configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing; wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.

A method includes obtaining multiple input image frames and determining how to warp at least one of the input image frames. The method also includes performing an intraband demosaic-warp operation to reconstruct image data in different color channels of the input image frames and warp the at least one input image frame to produce RGB input image frames. The method further includes blending the RGB input image frames to produce a blended RGB image frame, performing an interband denoising operation to produce a denoised RGB image frame, and performing an interband sharpening operation to produce a sharpened RGB image frame. In addition, the method includes performing an interband demosaic operation to substantially equalize high-frequency content in different color channels of the sharpened RGB image frame to produce an equalized sharpened RGB image frame and generating a final image of the scene based on the equalized sharpened RGB image frame.

An image processing method for converting a color space of input image data including color information is provided. The image processing method includes: preprocessing the input image data according to a frequency thereof to generate a preprocessed result; and color-converting the preprocessed result to generate output image data. The color space of the input image data is different from a color space of the output image data.

An image processing apparatus according to an embodiment includes a preprocessing circuit and a distortion correction circuit configured to process, in a time division manner, a plurality of images generated by a plurality of image pickup units and an output buffer circuit configured to buffer the processed plurality of images in a unit of a block and add an identification tag including an address and an identification ID to the block.

An image processing method including obtaining image data. The image data includes a plurality of image data values. The image processing method also includes processing the image data, thereby generating output data. Processing the image data includes applying a convolution operation to the plurality of image data values using a kernel including a plurality of coefficients. Applying the convolution operation includes obtaining a sum of image data values of the plurality of image data values that correspond respectively to coefficients of the plurality of coefficients that each have a common coefficient value. Applying the convolution operation also includes multiplying the sum by the common coefficient value.

An image capture device is described. The image capture device includes an array of pixels, each pixel including a photodetector. A Bayer pattern color filter is disposed over a 4×4 subset of pixels in the array of pixels. The Bayer pattern color filter defines a first 2×2 subset of pixels sensitive to red light; a second 2×2 subset of pixels sensitive to green light; a third 2×2 subset of pixels sensitive to green light; and a fourth 2×2 subset of pixels sensitive to blue light. A set of 1×1 on-chip lenses (OCLs) includes a different 1×1 OCL disposed over each pixel in the second 2×2 subset of pixels and the third 2×2 subset of pixels. A set of 2×1 OCLs or 2×2 OCLs includes a 2×1 OCL or a 2×2 OCL disposed over each pixel in the first 2×2 subset of pixels and the fourth 2×2 subset of pixels.

An image processing method, an electronic device, and a computer-readable storage medium are provided. In the method, a first image is processed through a U-net to obtain a second image, and the second image is a noise map of the first image. The U-net includes an encoding network, a decoding network, and a bottleneck network between the encoding network and the decoding network. The bottleneck network includes a global pooling layer, a bilinear upscaling layer, and a 1×1 convolutional layer. Moreover, a third image is generated according to the first image and the second image, and the third image is a denoised map of the first image.

An imaging apparatus includes a storage portion that stores captured image data obtained by imaging a subject by an imaging element and is incorporated in the imaging element, an output portion that is incorporated in the imaging element, and a plurality of signal processing portions that are disposed outside the imaging element, in which the output portion includes a plurality of output lines each disposed in correspondence with each of the plurality of signal processing portions and outputs each of a plurality of pieces of image data into which the captured image data stored in the storage portion is divided, to a corresponding signal processing portion among the plurality of signal processing portions from the plurality of output lines, and any of the plurality of signal processing portions combines the plurality of pieces of image data.