A method for inverse tone mapping includes obtaining a histogram of a low dynamic range image, called LDR image and obtaining an ITMO function, allowing obtaining a pixel value of a High Dynamic Range image, called HDR image, from a pixel value of the LDR image and a gain function depending on said pixel value of the LDR image. A search process is applied using the obtained histogram to identify areas of the LDR image producing bright areas in the HDR image when the ITMO function is applied on said LDR image. Information representative of the bright areas is used to determine when modifying the gain function to ensure the HDR image respects at least one predefined light energy constraint.

A computer-implemented method includes performing analysis to determine information associated with a high dynamic range (HDR) media content item. A standard dynamic range (SDR) version of the HDR media content item is derived using HDR metadata. The derivation including encoding the HDR media content item to SDR content and normalizing data of the SDR content. The HDR metadata and the SDR version of the HDR media content item are transmitted with embedding of the HDR metadata within a protocol with the SDR version to a storage service. The HDR metadata and the SDR version are caused to be received at a display device. The SDR version is converted to HDR per incremental portion with a dynamic range bounded based on the HDR metadata of the protocol.

The present disclosure provides a method and apparatus for converting an image format, an electronic device, a computer readable storage medium and a computer program product, relates to the field of artificial intelligence technology such as computer vision and deep learning, and can be applied to intelligent sensing ultra-definition scenarios. A specific implementation of the method includes: acquiring a to-be-converted standard dynamic range image; performing a convolution operation on the standard dynamic range image to obtain a local feature; performing a global average pooling operation on the standard dynamic range image to obtain a global feature; and converting the standard dynamic range image into a high dynamic range image according to the local feature and the global feature.

A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.

The present disclosure provides systems, apparatus, methods, and computer-readable media that support multi-frame depth-of-field (MF-DOF) for deblurring background regions of interest (ROIs), such as background faces, that may be blurred due to a large aperture size or other characteristics of the camera used to capture the image frame. The processing may include the use of two image frames obtained at two different focus points corresponding to the multiple ROIs in the image frame. The corrected image frame may be determined by deblurring one or more ROIs of the first image frame using an AI-based model and/or local gradient information. The MF-DOF may allow selectively increasing a depth-of-field (DOF) of an image to provide focused capture of multiple regions of interest, without causing a reduction in aperture (and subsequently an amount of light available for photography) or background blur that may be desired for photography.

This imaging signal processing apparatus includes: a first video signal processing unit that generates, from a pixel signal obtained by an imaging unit capable of obtaining a pixel signal with a first dynamic range, a luminance reduction video signal with a second dynamic range narrower than the first dynamic range; and a second video signal processing unit that generates, from the pixel signal, an information signal of a luminance change component of a luminance region of the luminance reduction video signal and a gain up signal of a pixel signal of the luminance region and adds the information signal and the gain up signal to the luminance reduction video signal.

Systems, apparatuses and methods may a performance-enhanced computing system comprising a sensor for measuring luminance values corresponding to light focused onto the sensor at a plurality of pixel locations, a memory including a set of instructions, and a processor. The processor executes a set of instructions causing the system to generate a multi-segment tone mapping curve, generate a set of tone mapping values corresponding to the multi-segment tone mapping curve for equally spaced input values between zero and one for storage into a look up table, and process the luminance values using the look up table to apply the tone mapping curve to the luminance values of the pixels.

An electronic device may include a camera, a display, and at least one processor. The at least one processor may be configured to display a first image obtained through the camera in a first area of the display, identify a plurality of areas included in the first image, identify a plurality of image effects applicable to the plurality of areas, display a plurality of second images to which the plurality of image effects are applied, respectively, in a second area adjacent to the first area, and display a third image resulting from applying an image effect corresponding to an image selected from among the plurality of second images to the first image.

A system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces are detected having threshold skin tone within a scene. Next, based on the detected one or more faces, the scene is segmented into one or more face regions and one or more non-face regions. A model of the one or more faces is constructed based on a depth map and a texture map, the depth map including spatial data of the one or more faces, and the texture map includes surface characteristics of the one or more faces. The one or more images of the scene are captured based on the model. Further, in response to the capture, the one or more face regions are processed to generate a final image.

A minimum luminance value of a display luminance information is larger than a minimum luminance value of a print luminance information. And in a conversion, a luminance value of a dark region of input image data is converted to a luminance value of a dark region of output image data such that a contrast of a dark region that includes the minimum luminance value of the print luminance information becomes closer to a contrast of a dark region that includes the minimum luminance value of the display luminance information.