Adaptive Colour Filter Array (CFA) Demosaicking with Mixed Order of Approximation

Adelaide, Australi

Color Filter Array Demosaicking Using High-Order Interpolation Techniques With a Weighted Median Filter for Sharp Color Edge Preservation

Demosaicking is an estimation process to determine missing color values when a single-sensor digital camera is used for color image capture. In this paper, we propose a number of new methods based on the application of Taylor series and cubic spline interpolation for color filter array demosaicking. To avoid the blurring of an edge, interpolants are first estimated in four opposite directions so that no interpolation is carried out across an edge. A weighted median filter, whose filter coefficients are determined by a classifier based on an edge orientation map, is then used to produce an output from the four interpolants to preserve edges. Using the proposed methods, the original color can be faithfully reproduced with minimal amount of color artifacts even at edges

CFA demosaicking with improved colour edge preservation

Los Alamitos, C

Image quality comparison between 3CCD pixel shift technology and single-sensor CFA demosaicking

This paper investigates the performance differences in taking measure of the difference in total pixel count between the 1.5M-pixel 3CCD with pixel shift technology and the 2M-pixel single image sensor using CFA demosaicking for full HD video capture in terms of image quality and color artifacts

A novel shape descriptor based on empty morphological skeleton

Los Alamitos, US

Adaptive order-statistics multi-shell filtering for bad pixel correction within CFA demosaicking

As today's digital cameras contain millions of image sensors, it is highly probable that the image sensors will contain a few defective pixels due to errors in the fabrication process. While these bad pixels would normally be mapped out in the manufacturing process, more defective pixels, known as hot pixels, could appear over time with camera usage. Since some hot pixels can still function at normal settings, they need not be permanently mapped out because they will only appear on a long exposure and/or at high ISO settings. In this paper, we apply an adaptive order-statistics multi-shell filter within CFA demosaicking to filter out only bad pixels whilst preserving the rest of the image. The CFA image containing bad pixels is first demosaicked to produce a full colour image. The adaptive filter is then only applied to the actual sensor pixels within the colour image for bad pixel correction. Demosaicking is then re-applied at those bad pixel locations to produce the final full colour image free of defective pixels. It has been shown that our proposed method outperforms a separate process of CFA demosaicking followed by bad pixel removal

Reduction of Colour Artifacts Using Inverse Demosaicking

Most digital cameras use a single image sensor to capture colour images. As a result, only one colour at each pixel location is acquired. Demosaicking is a technique to estimate all the other missing colour pixel information in order to produce a full colour image, while inverse demosaicking refers to the recovery of the single image sensor values from the full colour image. Early digital cameras using primitive demosaicking algorithms to produce a full colour image have resulted in inferior quality images with colour artifacts. Generally, the removal of those artifacts is not achievable by the application of direct filtering. If we can recover the actual image sensor values from a full colour image and re-demosaic it again using state-of-the-art recently developed demosaicking algorithms, a better image can be produced without filtering. In this paper, a novel technique using wavelet transform is proposed to inverse demosaic a full colour image in order to recover the actual sensor values. It is then re-demosaicked using an advanced recently developed demosaicking method to reproduce an output image with minimal colour artifacts

Pre-processing Techniques to Improve the Efficiency of Video Identification for the Pygmy Bluetongue Lizard

Copyright 2015 SCITEPRESS (Science and Technology Publications, Lda.). Published version of the paper reproduced here with permission from the publisherIn the study of the endangered Pygmy Bluetongue Lizard, non-invasive photographic identification is preferred to the current invasive methods which can be unreliable and cruel. As the lizard is an endangered species, there are restrictions on its handling. The lizard is also in constant motion and it is therefore difficult to capture a good still image for identification purposes. Hence video capture is preferred as a number of images of the lizard at various positions and qualities can be collected in just a few seconds from which the best image can be selected for identification. With a large number of individual lizards in the database, matching a video sequence of images against each database image for identification will render the process very computationally inefficient. Moreover, a large portion of those images are non-identifiable due to motion and optical blur and different body curvature to the reference database image. In this paper, we propose a number of pre-processing techniques for pre-selecting the best image out of the video image sequence for identification. Using our proposed pre-selection techniques, it has been shown that the computational efficiency can be significantly improved

Contrast enhancement by multi-level histogram shape segmentation with adaptive detail enhancement for noise suppression

This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited. This author accepted manuscript is made available following 24 month embargo from date of publication (November 2018) in accordance with the publisher’s archiving policyThe usual problems associated with image enhancement include over- and under-enhancement, halo effects at edges and the degradation of the signal-to-noise ratio as the enhancement of details increases. Some of those problems manifest in the background and some in the details of the enhanced image. Our proposed method is to apply different techniques to enhance the background and details separately. For enhancement of the image background, a novel multi-level histogram shape segmentation method which will detect abrupt changes in the histogram is proposed so that regions of intensity values with a similar frequency of occurrence are segmented for individual equalization to avoid over-enhancement. For detail enhancement, a novel adaptive median based enhancement method is applied to the details to avoid over- and under-enhancement while suppressing noise by limiting the degree of enhancement in homogeneous regions. Halo effects due to the over-enhancement of edges are avoided in our proposed method by using an edge preserving filter for the separation of the background and details so that edges are excluded from detail enhancement. It has been shown that our proposed method is able to avoid the usual adverse problems of image enhancement while producing adequate overall enhancement