Described are methods and devices for applying a color gamut mapping process on a first image to generate a second image, where the content of the first and second images is similar but the respective color spaces of the first and second images are different. The color gamut mapping process may be controlled using a color gamut mapping mode obtained from a bitstream where the color gamut mapping mode belongs to a set comprising at least two preset modes and an explicit parameters mode. If the obtained color gamut mapping mode is the explicit parameters mode and the color gamut mapping process is not enabled for the explicit parameters mode, the color gamut mapping process may be controlled by a substitute color gamut mapping mode determined from additional data.

A digital image sequence with multiple image frames can be enhanced. An appearance graph can be determined from the digital image sequence. The appearance graph includes prime layer nodes. Each prime layer node can represent a distinctive visual style. A prime layer image sequence can be computed for each prime layer node that matches the visual style represented by the prime layer node. An enhanced image sequence can be generated by blending at least two prime layer image sequences as defined by the appearance graph.

Systems and methods for tone mapping of high dynamic range (HDR) images for high-quality deep learning based processing are disclosed. In one embodiment, a graphics processor includes a media pipeline to generate media requests for processing images and an execution unit to receive media requests from the media pipeline. The execution unit is configured to compute an auto-exposure scale for an image to effectively tone map the image, to scale the image with the computed auto-exposure scale, and to apply a tone mapping operator including a log function to the image and scaling the log function to generate a tone mapped image.

The present invention provides image processing apparatus, an image processing system and an image processing method, whereby the accuracy of evaluation can be improved. Image processing apparatus for correcting a captured image includes an image acquisition unit that acquires block pixels, an image conversion unit that converts the lightness/darkness, density, luminance and color space of the image based on the RGB values contained in the block pixels, and an output unit that outputs the converted image, and the image conversion unit further includes a binarization processing unit that binarizes the image, calculates the area ration of lightness and darkness in the image, and specifies the light and dark fields, a density conversion unit that performs conversion into wavelengths corresponding to the RGB values, a luminance conversion unit that performs conversion into luminance of the maximum wavelength that is visible to human eye, and a color-space conversion unit that performs conversion into HSV values representing a color space of color tones.

In the technical solutions of a high dynamic range image synthesis method and an electronic device provided in embodiments of this application, a plurality of images with different depths of field in a current photographing scene are obtained based on an HDR photographing operation entered by a user, and each image has an exposure value. A plurality of images with a same exposure value are synthesized to generate a full depth-of-field image. Full depth-of-field images with a plurality of exposure values are synthesized by using an HDR algorithm to generate a high dynamic range image. Therefore, a high dynamic range image that is clear at each depth of field can be obtained, and a problem that a shallow depth of field leads to a blurred background and an insufficient dynamic range, and then results in overexposure or underexposure of a high dynamic range image can be resolved.

Systems and techniques are described for generating a high dynamic range (HDR) image. An imaging system can be configured to receive a first image captured by an image sensor according to a first exposure time. The imaging system can generate a modified image based on the first image by modifying the first image using a gain setting to simulate a second exposure time based on exposure compensation. The imaging system generates a high dynamic range (HDR) image at least in part by merging multiple images. The multiple images include a second-exposure image that corresponds to the second exposure time. The second-exposure image can be the modified image. The second-exposure image can be based on the modified image, processed variant of the modified image processed for noise reduction based on one or more other images actually captured using the second exposure time.

Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

A high-dynamic-range (HDR) detecting system includes a camera module comprising an image sensor; and a vision module that receives data generated by the camera module and accordingly transmits feedback to the camera module. The camera module comprises a multiple-exposure unit that provides different exposure settings, according to which a plurality of images are captured by the image sensor. The vision module comprises an object detection device that detects objects in the plurality of images, thereby resulting in object detection results as metadata.

An apparatus includes at least one memory configured to store instructions, and at least one processor in communication with the at least one memory and configured to execute the instructions to perform same area dividing on each of a plurality of images, and create a composite image from the plurality of images. A first area of the composite image is composited from corresponding areas in a first number of image among the plurality of images. A second area of the composite image is composited from corresponding areas in a second number of image among the plurality of images.

Disclosed are an image sensing device and an operating method thereof, and the image sensing device may include: an image sensor including a plurality of pixels and suitable for generating an image based on incident light; and an image processor suitable for generating a high dynamic range (HDR) image based on the image and two or more pieces of tone mapping information, which are divided according to luminance.