An image exposure device includes: an image display device that has a plurality of pixels and irradiates a photosensitive recording medium with light from the plurality of pixels; a support member that supports the photosensitive recording medium in a state in which an exposure surface of the photosensitive recording medium faces the image display device; a limiting member that is provided between the image display device and the support member and limits an angle of the light emitted from the image display device to the photosensitive recording medium; and a processor. The processor controls the image display device to display a display image generated by emphasizing only a dark portion among density differences of high-frequency components of an input image.

The invention provides a method of processing an image in a diagnostic apparatus 100 of diagnosing a disease using a captured image of an affected area, comprising: a memorizing step of memorizing the captured image (Step S12), and a processing step of processing the captured image memorized (Step S13), wherein in the processing step a region to be diagnosed is subjected to a highlighting process with a specified color thereof maintained.

A method and system detection of inherent noise present within a video source prior to digital video compression is disclosed. A noise image is extracted by subtracting a current image from its filtered version. Each pixel of the extracted noise image is normalized based on a determined principal edge image and the analog noise pixels are accumulated to generate an intermediate noise confidence value. Analog noise may be detected based on an analog noise confidence value generated based on the intermediate noise confidence value and a ringing metric, a blockiness metric, a motion vector cost of the current image, a blurriness exception weight, a flashiness exception weight, and a pan blur exception weight. The method may further comprise detection of high frequency noise based on determining a high frequency noise confidence value that may be based on a high frequency noise value and a frequency component with highest magnitude.

In an image processing apparatus, a detection unit detects a moving object from a captured image captured by an image capturing unit. An extraction unit extracts an edge from the captured image. A determination unit determines, based on a position of the moving object included in the captured image, whether to superimpose the edge on a region corresponding to the moving object in a background image. A generation unit generates an output image by superimposing a mask image, for obscuring the moving object, on the region in the background image and superimposing the edge, determined by the determination unit to be superimposed, on the region in the background image.

Various embodiments are provided which relate to the field of image signal processing, specifically relating to the generation of a depth-view image of a scene from a set of input images of a scene taken at different cameras of a multi-view imaging system. A method comprises obtaining a frame of an image of a scene and a frame of a depth map regarding the frame of the image. A minimum depth and a maximum depth of the scene and a number of depth layers for the depth map are determined. Pixels of the image are projected to the depth layers to obtain projected pixels on the depth layers; and cost values for the projected pixels are determined. The cost values are filtered and a filtered cost value is selected from a layer to obtain a depth value of a pixel of an estimated depth map.

Ophthalmological images generated by coherent imaging modalities have multiple types of noise, including random noise caused by the imaging system and speckle noise caused by turbid objects such as living tissues. These noises can occur at different levels in different locations. A noise-reduction method and system of the present disclosure thus relates to applying different filters for different types of noise and/or different locations of images, sequentially or in parallel and combined, to produce a final noise-reduced image.

An image enhancement method is provided. The method includes obtaining an image, extracting high-frequency components of the image, calculating an average pixel value of pixels corresponding to the extracted high-frequency components, and performing enhancement on pixels in the image that have pixel values greater than or equal to the calculated average pixel value to obtain an enhanced image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Deblurring a blurry image (14) includes the steps of (i) computing a spatial mask (256); (ii) computing a modified blurry image (264) using the blurry image (14) and the spatial mask (256); and (iii) computing a latent sharp image (16) using the modified blurry image (264) and a point spread function (260). Additionally, the image (714) to can be analyzed to identify areas of the image (714) that are suitable for point spread function estimation. Moreover, a region point spread function (1630) can be analyzed to classify the point spread function(s) as representing (i) motion blur, (ii) defocus blur, or (iii) mixed motion blur and defocus blur. A point spread function (2670) can also be estimated.

A multi-aperture imaging device that is, on the one hand, able to provide image information on a scene and, on the other hand, allows obtaining high lateral resolution and/or a wide total field of view, is described. The multi-aperture imaging device is provided with a first plurality of optical channels for projecting overlapping first partial fields of view of a total field of view on first image sensor areas of an image sensor of the multi-aperture imaging device, as well as with a second arrangement of optical channels for projecting at least a part of of the total field of view on a second image sensor area of the image sensor.

Provided are a method and device for image processing, a terminal device and a storage medium. The method includes: a high-brightness region is determined based on brightness of pixels in a first image, the brightness of the pixels in the high-brightness region being higher than the brightness of the pixels around the high-brightness region; a diffraction region in the first image is determined based on the high-brightness region, the diffraction region being an image region around the high-brightness region; and brightness of the diffraction region is reduced to obtain a second image. Through the method, after the brightness of the diffraction region is reduced, an overlap image formed by diffraction is alleviated, and the image is more real.