The present disclosure provides an image processing method and apparatus, an electronic device and a storage medium. The method includes: acquiring an initial image and a corresponding style image with brightness and chroma being separately represented; determining a first to-be-processed area in the style image, and determining a second to-be-processed area corresponding to the first to-be-processed area in the initial image; replacing a brightness component of the first to-be-processed area with a brightness component of the second to-be-processed area for the style image; filtering a chroma component of the first to-be-processed area for the style image; and generating an output image according to a processed style image.

An approach is provided for generating a super-resolution image as a higher resolution version of an input image. The approach, for example, involves determining a set of tasks to be performed on the input image to facilitate generating the super-resolution image. The approach also involves selecting a combination of loss functions, wherein each loss function of the combination of loss functions is respectively a task-specific neural network pre-trained to perform a corresponding one of the set of tasks. The approach also involves training the super resolution neural network using the combination of loss functions as one or more layers of the super resolution neural network. The approach also involves using the trained super resolution neural network to generate the super-resolution image as a higher resolution version of the input image.

Intensity values of electromagnetic radiation from an object to be imaged are received from an array of detectors. The array of detectors includes one or more pairs of detectors arranged as antisymmetric pairs of detectors. A Fourier transform of an image of the object is determined by correlating fluctuations of the intensity values for each antisymmetric pair of detectors. An inverse of the Fourier transform is determined, and an image of the object is generated from the inverse Fourier transform. The Fourier transform of the mean intensity pattern across the array of detectors may also be used to determine when the array is properly oriented to separate the image and mirror image.

An electronic device is provided. The electronic device may include at least one processor, and at least one memory. The at least one processor may be configured to execute the instructions stored in the at least one memory, to: acquire an input image, predict an inverse function of a monotonically increasing function for decreasing an image contrast ratio by applying the input image to a learning model trained by using an artificial intelligence algorithm, and acquire an output image based on the input image and the predicted inverse function of the monotonically increasing function. The learning model may be trained to predict the inverse function of the monotonically increasing function for decreasing the an image contrast ratio by using a training image generated by applying a target image having a high contrast ratio to the monotonically increasing function and the inverse function of the monotonically increasing function as training data.

An imaging system includes an image sensor configured to obtain first image data, based on a received light; and a processing circuit configured to determine an operating mode of the image sensor, among a first mode and a second mode, based on an illumination and a dynamic range corresponding to the obtained first image data. The image sensor includes a first sub-pixel configured to sense a target light corresponding to a target color, in the first mode, convert the target light sensed during a first exposure time, into a first signal, and in the second mode, convert the target light sensed during a second exposure time longer than the first exposure time, into a second signal.

During a period in which it is necessary to display two or more images in a superimposed manner, an image generation block of an information processing apparatus supplies the data of the image concerned to a superimposition processing block. A first image acquisition block and a second image acquisition block of the superimposition processing block acquire a first image and a second image, respectively, to be displayed in a superimposed manner. A luminance range adjustment block adjusts at least one luminance range as required with amount of adjustment corresponding to an alpha () value set to the second image. An output value computation block executes the computation of alpha blending by use of the image after adjustment so as to determine the pixel value of a display image, thereby outputting the determined pixel value to an image output block.

A method is provided. The method includes receiving at least part of an input image file. The at least part of the input image file includes input image data representing an image and reference tone mapping strength data representing a reference tone mapping strength parameter for deriving an input value representing an amount of spatially-variant tone mapping. The method further includes inputting the input image data and the input value to a tone mapping operation. The tone mapping operation is applied to the input image data to generate output image data representing an output image with the amount of spatially-variant tone mapping applied. A computing device is also provided.

A method for encoding video data into a video bitstream using a video capture device having a brightness range limited output determines capture conditions for the capture device, the capture conditions including an ambient capture light level and a measured light level of captured video data. The method adjusts a brightness adaptation model using at least the measured light level and the ambient capture light level, the brightness adaption model defining a temporally variable peak luminance for a viewer of video captured using the capture device, and then determines a tone map such that where the measured light level exceeds a determined maximum light level the tone map is modified to reduce brightness, the maximum light level is determined using the brightness adaptation model. The captured video data is then encoded into the video bitstream using the determined tone map.

An imaging system and method are provided in which a well of a microplate 050 is imaged by a camera 110 comprising magnification optics 112. The camera is controlled to acquire a series of images of the well with different exposures. The series of images comprise a base image with a base exposure and at least one further image with a larger exposure than the base exposure. The series of images are then merged into an output image which comprises in a center region of the well image content from the base image and at a peripheral region of the well image content from the at least one further image. Advantageously, the output image may allow for better assaying or analysis of the samples in the well than any of the individual images.

Apparatus for binning an input value into one of a plurality of bins which collectively represent a histogram of input values, each of the plurality of bins representing a corresponding range of input values, the apparatus comprising: an input for receiving an input value; a noise source configured to generate an error value according to a predetermined noise distribution; and a binning controller configured to mix the received input value with the error value so as to generate a modified input value and to allocate the modified input value to the bin corresponding to that modified input value.