Data adaptive filters for demosaicking: a framework

This paper presents a new weighted interpolation technique to address the Bayer Color Filter Array (CFA) demo-saicking problem. The proposed technique provides two contributions that have not been reported in conventional interpolation methods. Firstly, it exploits the quincuncial nature of the green component of the Bayer CFA. The second contribution treats each of the three planes or lattices of the CFA mosaic as distinct and is weighted uniquely. The proposed technique is compared with other interpolation-based demosaicking algorithms and a significant improvement in performance has been noted through experimental results. In addition, to provide objectivity in analysis, three test measures have been used — CPSNR, CIELAB and CIEDE2000.

The advent of efficient short range radio communication coupled with advancement in miniaturization of computing devices has enabled the development of wireless multimedia sensor network (WMSN). Energy is one of the scarcest resources in such networks, especially it is scarce in transmitting multimedia data. This work presents an energy-saving image compression scheme. The nodes in WMSN are equipped with Colour Filter Array (CFA) using Bayer Pattern. Considering alternate column dropping of pixel array of the captured image is one of the viable techniques for low-overhead image comprssion, Bayer CFA lacks in features by means of which image pixel may be reconstructed using its neighbouring pixel information. So primarily we design a new CFA (NCFA) to overcome the said problems of Bayer CFA. Subsequently, the compression scheme is proposed where the alternate columns of the pixel array of each of the macroblocks of an image captured through NCFA, are dropped. At the high-end receiving node (e.g. sink), image is reconstructed from the sequentially transmitted macroblocks. Performance of the scheme is evaluated and compared through qualitative and quantitative analyses. The results show the present scheme's dominance over all the variants of the competing scheme in terms of both energy-efficiency and reconstruction quality.

Most snapshot color imaging devices adopt a single sensor with a Bayer color filter array (CFA), where only one RGB component is captured at each pixel, requiring demosaicking to recover full-color images. Existing methods range from fast interpolation to deep learning approaches with high reconstruction accuracy but often suffer from a trade-off between performance and computational complexity. To address this issue, we propose an efficient demosaicking method based on dynamic weight learning. The proposed network adaptively computes layer-wise feature weights without increasing model parameters and incorporates a mixed-attention block to jointly exploit global and local information, effectively reducing reconstruction artifacts. Extensive experiments demonstrate superior performance over that of existing methods.

Non-Bayer color filter array (CFA) sensors have recently drawn attention due to their superior compression of spectral energy, ability to deliver improved signal-to-noise ratio, or ability to provide high dynamic range (HDR) imaging. Demosaicking methods that perform color interpolation of Bayer CFA data have been widely investigated. However, a bottleneck to the adaption of emerging non-Bayer CFA sensors is the unavailability of efficient color-interpolation algorithms that can demosaick the new patterns. Designing a new demosaick algorithm for every proposed CFA pattern is a challenge. In this paper, we propose a hardware-friendly universal demosaick algorithm based on maximum a-posteriori (MAP) estimation that can be configured to demosaick raw images captured using a variety of CFA sensors. The forward process of mosaicking is modeled as a linear operation. We then use quadratic data-fitting and image prior terms in a MAP framework and pre-compute the inverse matrix for recovering the full RGB image from CFA observations for a given pattern. The pre-computed inverse is later used in real-time application to demosaick the given CFA pattern. The inverse matrix is observed to have a Toeplitz-like structure, allowing for hardware-efficient implementation of the algorithm. We use a set of 24 Kodak color images to evaluate the quality of our demosaick algorithm on three different CFA patterns. The PSNR values of the reconstructed full-channel RGB images from CFA samples are reported in the paper.

The paper presents hardware implementation aspects of new efficient image compression algorithm designed for wireless capsule endoscopy with Bayer color filter array (CFA). Since power limitation, small size conditions and specific image data format (CFA) exclude application of traditional image compression techniques dedicated ones are necessary. Discussed algorithm is based on integer version of discrete cosine transform (DCT). Therefore it has low complexity and power consumption. It is demonstrated that the perfor-mance of proposed algorithm is comparable to the performance of JPEG2000 --- very complex, sophisticated wavelet based coder. In the paper a VLSI coder architecture is proposed and power requirements are discussed.

The Bayer Color Filter Array (CFA) is commonly used in such industries as digital cameras. However, due to the arrangement of color channels in the Bayer CFA, it becomes a problem to estimate the missed color information in each pixel. The algorithms that deal with this problem are named "demosaicking algorithms". There are many demosaicking algorithms, which show different efficiency and image quality for different images. This paper proposes an algorithm that combines two existing algorithms to reach better image qualities and acceptable computing complexities. The experimental results indicate effectiveness in terms of the balance between complexity and quality.

The Bayer Color Filter Array (CFA) is commonly used in such industries as digital cameras. However, due to the arrangement of color channels in the Bayer CFA, it becomes a problem to estimate the missed color information in each pixel. The algorithms that deal with this problem are named algorithms. There are many demosaicking algorithms, which show different efficiencies and image qualities for different images. This paper proposes an algorithm that combines two existing algorithms to reach better image qualities and acceptable computing complexities. The experimental results indicate effectiveness in terms of the balance between complexity and quality.

A new zooming algorithm suitable for single-sensor imaging devices is introduced. The proposed method is capable of zooming and enlarging data acquired by single sensor cameras operating on the Bayer color filter array (CFA) pattern. The approach allows for operations on noise-free data at the hardware level. Complexity and cost implementation are thus greatly reduced. Based on a color difference model and an adaptive edge-sensing mechanism, the proposed method produces sharp, visually pleasing outputs. At the same time, it yields excellent performance, in terms of both subjective and objective image quality measure.

In order to shrink pixel sizes for mobile phone cameras, several shared-pixel architectures have previously been proposed [1–3]. However, the use of conventional shared-pixel architectures leads to a Gr/Gb sensitivity imbalance because the Gr pixel and the Gb pixel have a different layout structure when using a Bayer Color Filter Array (CFA) [3,4]. The Gr/Gb sensitivity imbalance causes conspicuous fixed-pattern noise in reproduced images [4]. In many cases, the noise level has a scene-color temperature dependence, due to the sensitivity imbalance caused by the attenuation of the incident light by the electromagnetic effect. Therefore, conventional shared-pixel architectures need complicated digital signal processing to properly correct for this Gr/Gb sensitivity imbalance.

We introduced a digital photo image analysis in color space to estimate the spectrum of fluor components dissolved in a liquid scintillator sample through the hue and wavelength relationship. Complementary metal oxide semiconductor (CMOS) image sensors with Bayer color filter array (CFA) technology in the digital camera were used to reconstruct and decode color images. Hue and wavelength are closely related. To date, no literature has reported the hue and wavelength relationship measurements, especially for blue or close to the UV region. The non-linear hue and wavelength relationship in the blue region was investigated using a light emitting diode source. We focused on this wavelength region, because the maximum quantum efficiency of the bi-alkali photomultiplier tube (PMT) is around 430 nm. It is necessary to have a good understanding of this wavelength region in PMT-based experiments. The CMOS Bayer CFA approach was sufficient to estimate the fluor emission spectrum in the liquid scintillator sample without using an expensive spectrophotometer.

This paper introduces a novel lensless full colour diffractive computational imaging system with a planar Multilevel Phase Mask (MPM) as a diffractive optical element (DOE). The novelty concerns: a methodology of MPM design for improved depth of focus (DoF); design of PSFs for RGB imaging and an inverse imaging algorithm with sparse colour image modelling simultaneous for all RGB channels. MPMs are step-wise invariant. The cubic wavefront coding (WFC) is incorporated in MPMs with optimization of number of levels and width of invariant steps. This design of MPM makes the system robust with respect to defocus (improves DoF) and diminish chromatic aberrations typical for DOEs. Broadband multichannel test-images are exploited for design and testing of the lensless system. We consider two alternative optical setups: Wavelength Multiplexing (WM) and Wavelength Division (WD). In WM, the light beam is broadband multichannel with light sources radiating all wavelengths simultaneously and a CMOS sensor is equipped with a Bayer colour filter array (CFA) for registration of spectral measurements. In this setup, a single MPM is designed for the broadband multichannel light beams. In WD, separate exposures of RGB channels are registered by a broadband grey-scale CCD sensor. Different MPMs are designed for each of the RGB channels. Simulation experiments demonstrate the essentially extended DoF of the designed lensless systems and the advanced accuracy and quality of imaging with respect to the corresponding WM and WD systems with refractive lenses. Due to robustness of the designed lensless system to chromatic aberrations, this advantage has a place even with respect to the lens-system.

To save the hardware cost, most modern digital video cameras employ the single-sensor technology together with the red-green-blue (RGB) color filter array (CFA) structure to capture real-world scenes. Each pixel in the captured video is only composed of one primary color. This kind of videos is usually referred to be the CFA video (or mosaic video). For the purpose of visual display, it is necessary to convert the captured CFA video into the full-color RGB video. In this paper, we propose a novel quality-efficient universal demosaicing for arbitrary CFA videos. In the proposed method, both the spatial and the temporal correlations among the CFA data are taken into account during the demosaicing. Then an effective least square-based approach is developed to fuse the spatial-based demosaiced result and the temporal-based one. Experimental results on 27 test videos with ten typical RGB CFA structures show that the demosaiced videos generated by the proposed universal demosaicing method yield better quality in terms of average color peak signal-to-noise ratio (CPSNR) and demonstrate less color artifacts in terms of visual perception in comparison with two state-of-the-art universal demosaicing methods.

This paper describes a demosaicking method for hexagonally-structured color filter array. Demosaicking is essential to acquire color images using color filter array (CFA) in single sensor imaging. Thus, CFA patterns have been discussed in order to improve image quality in single sensor imaging after the Bayer pattern are introduced. Advancements in imaging sensor technology recently introduce a hexagonal CFA pattern. The hexagonal CFA can be considered to be a 45-degree rotational version of the Bayer pattern, thus demosaicking can be implemented by an existing method with backward and forward 45-degree rotations. However, this approach requires heavy computing power and memory in image sensing devices because of the image rotations. To overcome this problem, we proposes a demosaicking method for a hexagonal Bayer CFA without rotations. In addition, we introduce a weighting parameter in our demosaicking method to improve image quality and to unifying exiting method with our method. Experimental results indicate that the proposed method is superior to conventional methods in terms of PSNR. In addition, some optimized values for the weighting parameter are provided experimentally.

We present a computationally efficient demosaicing algorithm based on a luminance-chrominance model of the color filter array (CFA) image. We show that the chrominance information can be estimated using simple low-pass filtering. This algorithm allows us to use separable recursive filters, which are particularly adapted for real-time processing. Moreover, while most of demosaicing algorithms are specific to a particular CFA (usually the popular Bayer CFA), our method can be applied to any CFA. We present a linear version of the algorithm and an adaptive extension.

Common consumer RGB cameras use a single image sensor to capture RGB color images with a color filter array (CFA) placed in front of the image sensor. This system captures particularly one color sample ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> , or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> ) at each pixel location. This captured raw image is called a CFA image. The CFA demosaicking is used then to construct the complete color image. A similar approach of working with a single image sensor and extension of CFA to multispectral filter array (MSFA) enables us to develop a low-cost multispectral camera with the help of MSFA demosaicking. However, due to many spectral bands and their very sparse sampling, extending CFA demosaicking to MSFA demosaicking is not easy. This paper proposes an adaptive and progressive MSFA demosaicking method for various MSFA patterns for 5 to 15 bands multispectral images. Binary tree-based MSFA patterns are used to create MSFA images, and these MSFA patterns can be designed for any N-band MSFA image. The spectral bands are arranged specifically so that the middle band has the highest probability of appearance (PoA) in the MSFA pattern. The middle band is interpolated first using PoA based progressive adaptive interpolation, and the modified bilinear spectral difference method is used to estimate all other spectral bands with the interpolated middle band’s help. Experimental results and comparative analysis on two different datasets reveal that our proposed MSFA demosaicking method outperforms the existing generic MSFA demosaicking methods in terms of computational time and various image quality metrics considered.