An imaging system and a method of creating composite images are provided. The imaging system includes one or more lens assemblies coupled to a sensor. When reflected light from an object enters the imaging system, incident light on the metalens filter systems creates filtered light, which is turned into composite images by the corresponding sensors. Each metalens filter system focuses the light into a specific wavelength, creating the metalens images. The metalens images are sent to the processor, wherein the processor combines the metalens images into one or more composite images. The metalens images are combined into a composite image, and the composite image has reduced chromatic aberrations.

An image processing method includes: converting a red yellow blue RYB image into a grid image based on a red green blue RGB format; generating a first brightness layer of the grid image, and determining a reference gain compensation array used to adjust the first brightness layer; obtaining, based on a preset compensation array correspondence, a target gain compensation array that is associated with the reference gain compensation array and based on an RYB format; and adjusting a second brightness layer of the RYB image by using the target gain compensation array, to generate a corrected image.

Provided is a method and a device for correcting lateral chromatic aberration, a storage medium and a computer equipment. In the method, a relationship model between lens position and magnitude of LCA is constructed based on preset parameters of lens positions, and the relationship model is stored as calibration data; system parameters of a camera to be corrected and pre-stored calibration data are obtained; the LCA of the camera to be corrected is obtained by calculating the system parameters; and the LCA is corrected by the calibration data. With the method, the LCA of the lens can be removed when the focus distance is changed, and the method is suitable for mass-production.

A method for suppressing chromatic aberration, especially blue or red fringing, in a digital image with multiple color channels is disclosed. The method comprises negatively correcting a first color channel by subtracting an overshoot component of the first color channel. The subtraction is subject to a lower threshold, which is dependent on a local value of at least one further color channel.

A method of calibrating an image sensor includes obtaining a plurality of multi-light source images generated from a plurality of image sensor modules, wherein each of the plurality of image sensor modules generates at least three multi-light source images; obtaining, based on the plurality of multi-light source images, a plurality of crosstalk levels and a plurality of color-specific correction coefficients; generating modeling data based on a relationship between a crosstalk level for a first color and the plurality of color-specific correction coefficients; and obtaining, based on a single light source image captured by a first image sensor module and the modeling data, a color-specific correction coefficient for a target pixel of an image sensor, the image sensor being provided in the first image sensor module, and the color-specific correction coefficient being usable for correcting a crosstalk of the target pixel.

An imaging apparatus includes an autofocus function, and performs focus adjustment by displacing a focus lens to an in-focus opposition. A focal correction calculation unit calculates a focal correction amount using at least one type of information selected from the diaphragm information used for exposure adjustment, positional information for the zoom lens, and positional information for the focus lens. The focal correction amount is further revised, and processing is executed to suppress coloring on the subject image resulting from chromatic aberration. The correction amount after revision is sent to a focal adjustment unit and the focal lens is driven and controlled by the lens control unit.

In the advanced driver-assistance systems (ADAS) field, RAW sensor image processing for machine vision (MV) applications can be of critical importance. Due to red/green/blue (RGB) image components being focused by the lens at different locations in image plane, the lateral chromatic aberration (LCA) phenomenon may sometimes be observed, which causes false color around edges in the final image output, especially for high contrast edges, which can impede MV applications. Disclosed herein are low-latency, efficient, optimized designs for chromatic aberration correction (CAC) modules. In some embodiments, an in-pipeline CAC design is used that: is configured to perform on-the-fly CAC without any out-of-pipeline memory traffic; enables use of wide dynamic range (WDR) sensors; uses bicubic interpolation; supports vertical and horizontal chromatic aberration red/blue color channel offsets, reduces CAC line memory requirements, and supports flexible look-up table (LUT) down-sampling factors to improve the spatial precision of correction and accommodate popular image sensor resolutions.

In the advanced driver-assistance systems (ADAS) field, RAW sensor image processing for machine vision (MV) applications can be of critical importance. Due to red/green/blue (RGB) image components being focused by the lens at different locations in image plane, the lateral chromatic aberration (LCA) phenomenon may sometimes be observed, which causes false color around edges in the final image output, especially for high contrast edges, which can impede MV applications. Disclosed herein are low-latency, efficient, optimized designs for chromatic aberration correction (CAC) modules. In some embodiments, an in-pipeline CAC design is used that: is configured to perform on-the-fly CAC without any out-of-pipeline memory traffic; enables use of wide dynamic range (WDR) sensors; uses bicubic interpolation; supports vertical and horizontal chromatic aberration red/blue color channel offsets, reduces CAC line memory requirements, and supports flexible look-up table (LUT) down-sampling factors to improve the spatial precision of correction and accommodate popular image sensor resolutions.

When distortion correction is performed by dividing a distortion correction target region (Atc), a distortion-corrected division region image (D3) is generated by performing the distortion correction on each division region of the distortion correction target region and a distortion-corrected image (D4) is generated by combining a plurality of distortion-corrected division region images (D3). Regarding each pixel of the distortion-corrected image (D4), a gain (Gp) is determined according to scaling ratios (MR) of a division region including the pixel and one or more division regions around the division region, high-frequency components (D6) of the pixel are multiplied by the gain (Gp), and the product is added to a pixel value of the pixel of the distortion-corrected image (D4). This makes it possible to lessen the difference in the sense of resolution among the division regions of the distortion-corrected image (D4) and obtain an image having an excellent sense of resolution.

An example apparatus for image processing includes a motion-adaptive image enhancer to receive motion data and an image with reduced noise. The motion-adaptive image enhancer can process an occluded region of the image based on the motion data. The motion-adaptive image enhancer can generate an enhanced image.