Abstract:
An image processing apparatus and an image processing method which can transmit/receive or display a high-dynamic-range image suitably are provided. An image transmission apparatus transfers, as metadata of content, luminance conversion information, which is for conversion of HDR content (for example, with maximum luminance 2000 nit) into SDR content (having, for example, 100 nit), or differential information. Based on the metadata, an image reception apparatus generates luminance conversion information, in which received HDR content is adapted to a capability of a display (for example, with maximum luminance 500 nit or 1000 nit) in an output destination, and realizes display mapping which is not against an intention of a producer.
Abstract:
The invention relates to an image processing method for filtering with an adaptive filter core size, characterized in that the following steps are included: (a) an original image is created, (b) an information measure is calculated on the basis of the original image, (c) a filter core size is calculated on the basis of the information measure, (d) the original image is low-pass filtered with an adaptive low-pass filter with filter core size to form a low-pass filtered image, (e) a high-pass filtered image is calculated by subtracting the low-pass filtered image from the original image, (f) a detail -enhanced image without light rings is obtained by a high-pass image scaled with a detail enhancement measure being added to the low-pass image. The invention additionally relates to an image processing device comprising an image recording device 11), an image processing unit (12), and an image display unit (13), in which: (a) the recording device (11) creates an original image, (b) an image processing unit (12) calculates an information measure on the basis of the original image, (c) an image processing unit (12) calculates a filter core size on the basis of the information measure, (d) the image processing unit (12) low-pass filters the original image with an adaptive low-pass filter with filter core size to form a low-pass filtered image, (e) the image processing unit (12) calculates a high-pass filtered image by subtracting the low-pass filtered image from the original image, (f) the image processing unit (12) calculates a detail -enhanced image without light rings by adding a high-pass image scaled with a detail enhancement measure to the low-pass image, (g) the image display unit (13) visualizes the detail- enhanced image without light rings.
Abstract:
Diagnostic display system comprising a database, digital data processing device and display. The database includes (1) measurement- based diagnostic data characterizing a patient's body; (2) denoising algorithms corresponding values of denoising parameters; and (3) enhancement algorithms corresponding to values of enhancement parameters. The digital data processing device is operatively coupled to the database and configured to (1) receive a denoising and enhancement value from \client input device; (2) based on the denoising value, apply the corresponding denoising algorithm to the diagnostic data to generate denoised diagnostic data; (3) based on the enhancement value, apply the corresponding enhancement algorithm to the denoised diagnostic data to generate enhanced denote ed diagnostic data; and (4) generate denoised and enhanced denoised images based on the respective denoised and enhanced denoised diagnostic data. The display is operatively coupled to the digital data processing device and configured to simultaneously display the denoised and enhanced denoised diagnostic data.
Abstract:
Systems and methods for perceptual image preview are described. In one aspect, a naïve preview image is generated by downsampling a larger image. Perceptual features of the larger image are then detected. Information associated with the detected perceptual features is then incorporated into the naïve preview image to create a perceptual preview image. Since the perceptual preview image incorporates information associated with the detected perceptual features, a viewer of the perceptual preview image will be more likely to detect the presence or absence of such perceptual features in the larger image.
Abstract:
A pixel-based image (3) is analysed by scanning the image in a predetermined direction (15) from a predetermined point along a first edge (9) of the image towards a second edge (11). The brightness of the first pixel is determined and a value for a background value is established on the basis of the brightness of the first pixel. The brightness of each subsequent pixel is then tested in the predetermined direction (15) and, if any such subsequent pixel is found to have a value which differs substantially from the background value, substituting the tested value for the background value. After a predetermined number of tested pixels have been found to have substantially the same background value, each subsequent pixel is tested in the following manner: a value for a foreground value is established on the basis of the tested pixel if the tested pixel is found to have a value which differs from the background value by more than a predetermined amount; and a threshold value is determined on the basis of the foreground value and the background value after a predetermined number of foreground pixels and a predetermined number of background pixels have been identified.
Abstract:
Denoising such as discrete universal denoising (DUDE) that scans a noisy signal in an attempt to characterize probabilities of finding symbol values in a particular context in a clean signal can perform a rough denoising (1230) on the noisy signal and identify contexts from a roughly denoised signal. The rough denoising (1230) improves estimation of the statistical properties of the clean signal by reducing the false differentiation of contects that noise can otherwise create. Statistical information regarding occurrences of symbols in the noisy signal and corresponding contexts in the roughly denoised signal can then be used to denoise the noisy signal. The specifics of the rough denoising (1230) can be chosen based on knowledge of the noise or of clean date Alternatively, the DUDE can be used in an iterative fashion where the denoised signal produced from a prior iteration provides the contexts for the next iteration.
Abstract:
In some embodiments, the invention includes a method for locating text in digital images. The method includes scaling a digital image into images of multiple resolutions and classifying whether pixels in the multiple resolutions are part of a text region. The method also includes integrating scales to create a scale integration saliency map and using the saliency map to create initial text bounding boxes through expanding the boxes from rectangles of pixels including at least one pixel to include groups of at least one pixel adjacent to the rectangles, wherein the groups have a particular relationship to a first threshold. The initial text bounding boxes are consolidated. In other embodiments, a method includes classifying whether pixels are part of text region, creating initial text bounding boxes, and consolidating the initial text bounding boxes, wherein the consolidating includes creating horizontal projection profiles having adaptive thresholds and vertical projection profiles having adaptive thresholds.