- #BANDING IN HELICON REMOTE PATCH#
- #BANDING IN HELICON REMOTE VERIFICATION#
- #BANDING IN HELICON REMOTE SOFTWARE#
All pixels are classified into three kinds of regions (according to the number of peaks in the considered focus profile) and different fusion rules are applied to different kinds of regions. The focus profile of each pixel is then calculated from the detailed high-frequency sub-bands. A two-level DWT (Digital Wavelets Transform) is first applied to each frame of the focal stack. also proposed region-adaptive fusion from focal stack images.
In their work, region definitions are however fixed and not further refined. The “average image” calculated from the focal stack is incurred segmentation by means of the well-known mean-shift algorithm to define initial regions. To solve this problem, proposed a region-based algorithm, where the focus measure is calculated for each segmented region of arbitrary shape. For block- or patch-based algorithms, a regular shape often results in blocking or ringing artifacts and probably fails near region boundaries. Other methods include post-optimization on the resulting weight maps before image fusion is performed. proposed a selective weighting scheme (a linear combination of selected pixels with higher focus measures) so as to reduce the noise in the AIF image. However, these kinds of methods will yield a low-quality or noisy AIF image in the presence of noise. For pixel-based algorithms, a pixel in the AIF image is often calculated as a weighted average of the collocated pixels in the original focal stack. This kind of categorization depends on the area where a focus measure is computed. The above metrics might still result in higher focus measurements for blurred or smooth regions due to noise or image degradation, which will certainly degrade the reconstructed AIF image when the maximum selection rule is adopted.ĭFF algorithms can be categorized into pixel-, block-, and region-based.
#BANDING IN HELICON REMOTE VERIFICATION#
However, their proposed metric is still misjudged on smooth regions and needs a block-based consistency verification procedure for correction. present a Multi-scale Image Analysis (MIA) technique to determine the focusing properties of input image pixels. In, the surface areas of the enclosed region around a same given pixel in different focused input images are computed and compared, as a measure, to distinguish focused and blurred regions.
In, the modulation transfer function (MTF) is calculated as a ratio between the image contrast and sharpness to indicate focus metric. Image quality measure (IQM) was adopted by calculating the average of gradients for pixels within a window. , however, apply Gaussian (low-pass) filtering to blur the target image and then compare the blurred result with the original one the difference can then be used to reveal the focus level of the original image. Traditional transforms often estimate the spatially high frequency information in a local window to indicate the focusing level, e.g., Laplacian filtering and the variation approaches.
#BANDING IN HELICON REMOTE PATCH#
The resulting energy of the transformed patch is then calculated as the focus level estimation. The focus measure operator often concerns a transformation of the original image patch to enhance its sharpness. The success of DFF/AIF techniques rely on a reliable focus measure for image patches.
#BANDING IN HELICON REMOTE SOFTWARE#
Our experiments show that the adaptive region-splitting algorithm outperforms other state-of-the-art methods or commercial software in synthesis quality (in terms of a well-known Q metric), depth maps (in terms of subjective quality), and processing speed (with a gain of 17.81~40.43%). Regions whose focus profiles are not confident in getting a winner of the best focus will resort to spatial propagation from neighboring confident regions. The depth image can be easily converted from the resulting label image, where the label for each pixel represents the image index from which the pixel with the best focus is chosen.
After iterative splitting, the final region map is used to perform regionally best focusing, based on the Winner-take-all (WTA) strategy, i.e., choosing the best focused pixels from image stack. Spatial-focal property for each region is then analyzed to determine whether a region should be iteratively split into sub-regions. An initial all-focus image is first computed, which is then segmented to get a region map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated based on irregularly shaped regions that have been refined or split in an iterative manner, to adapt to different image contents. In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map.
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