Abstract:
Multiple-exposure high dynamic range image processing may be performed that filters pixel values that are distorted by blooming from nearby saturated pixels. Pixel values that are near saturated pixels may be identified as pixels that may be affected by blooming. The contributions from those pixels may be minimized when producing a final image. Multiple-exposure images may be linearly combined to produce a final high dynamic range image. Pixel values that may be distorted by blooming may be given less weight in the linear combination.
Abstract:
Adaptive local tone mapping may be used to convert a high dynamic range image to a low dynamic range image. Tone mapping may be performed on an on a Bayer domain image. A high dynamic range image may be filtered to produce a luminance signal. An illumination component of the luminance signal may be compressed. A reflectance component of the luminance signal may be sharpened. After the luminance signal has been processed, it may be used in producing an output image in the Bayer domain that has a lower dynamic range than the input image. The output Bayer domain image may be demosaiced to produce an RGB image. Tone-mapping may be performed with a tone-mapping processor.
Abstract:
This is generally directed to systems and methods for noise reduction in high dynamic range (“HDR”) imaging systems. In some embodiments, multiple images of the same scene can be captured, where each of the images is exposed for a different amount of time. An HDR image may be created by suitably combining the images. However, the signal-to-noise ratio (“SNR”) curve of the resulting HDR image can have discontinuities in sections of the SNR curve corresponding to shifts between different exposure times. Accordingly, in some embodiments, a noise model for the HDR image can be created that takes into account these discontinuities in the SNR curve. For example, a noise model can be created that smoothes the discontinuities of the SNR curve into a continuous function. This noise model may then be used with a Bayer Filter or any other suitable noise filter to remove noise from the HDR image.
Abstract:
High dynamic range image sensors and image reconstruction methods for capturing high dynamic range images. An image sensor that captures high dynamic range images may include an array of pixels having two sets of pixels, each of which is used to capture an image of a scene. The two sets of pixels may be interleaved together. As an example, the first and second sets of pixels may be formed in odd-row pairs and even-row pairs of the array, respectively. The first set of pixels may use a longer exposure time than the second set of pixels. The exposures of the two sets of pixels may at least partially overlap in time. Image processing circuitry in the image sensors or an associated electronic device may de-interlace the two images and may combine the de-interlaced images to form a high dynamic range image.
Abstract:
A ringing area detector classifies the input image into two regions: a mosquito noise region (i.e. filtering region) and a non-mosquito noise region (i.e. non-filtering region), and uses this classification information to adaptively remove the mosquito noise in a mosquito noise reduction system. The mosquito noise reduction system includes a ringing area detector, a local noise power estimator, a smoothing filter, and a mixer. The ringing area detector includes an edge detector, a near edge detector, a texture detector, and a filtering region decision block. The ringing detection block detects the ringing area where the smoothing filter is to be applied. The local noise power estimator controls the filter strength of the smoothing filter. The smoothing filter smoothes the input image. The mixer mixes the smoothed image and the original image properly based on the region information from the ringing area detection block.
Abstract:
A video noise reduction system for reducing video noise in a sequence of video frames. In the video noise reduction system, a temporal filter computes multiple temporal average values for the video frames in different temporal directions. A motion detector computes multiple motion signal values for the video frames in different temporal directions. Finally, a control unit selects one of the temporal average values based on the motion signal values as output.
Abstract:
An improved method for color transient enhancement in an input video frame of pixels. The luminance value of a current pixel is compared to that of neighboring pixels. A correction value is determined and the chrominance value of the current pixel is “pushed” towards the neighboring pixel that has a luminance value closest to that of the current pixel, by adding the correction value to the current pixel's chrominance value. The original video frame is also separately processed using a CTI method, and the current pixel's corrected chrominance value is combined with the corresponding pixel in the output of the CTI processing by soft switching unit to generate an output video frame that is an enhanced version of the input video frame.
Abstract:
A superior Color Transient Improvement technique is adaptive to the local image features, so that more natural color edge transition improvement can be accomplished. A gain control function is provided that depends on the local image feature so that different regions of the image can be treated differently. Further, a correction signal is controlled in such a way (by the local image feature) that neither undershoot nor overshoot occurs, eliminating the need for post-processing for undershoot/overshoot removal.