Color artifacts of demosaicked images are often found at contours due to interpolation across edges and cross-channel aliasing. To tackle this problem, we propose a novel demosaicking method to reliably reconstruct color channels of a Bayer image based on two different optimized mean-curvature (MC) models. The missing pixel values in green (G) channel are first estimated by minimizing a variati...

A new Bayer pattern demosaicking scheme for single-sensor digital cameras is introduced. The raw output from a sensor, mostly a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) sensor, with a Bayer filter represents a mosaic of red, green and blue pixels of different intensity. To interpolate the two missing color components in each spatial location and constitute...

This paper presents a new adaptive window-based guided filtering and interpolation for polarization image demosaicing. Considering the correlation between channels, authors use total intensity S0 as guide to of all channels. The is performed with window size selection technique. A novel iterative residual proposed construct accurate full resolution polarization. Experiments are conducted sample...

Division-of-focal-plane (DoFP) polarization image sensors allow for snapshot imaging of linear effects with inexpensive and straightforward setups. However, conventional interpolation based reconstruction methods such produce unreliable noisy estimates quantities as Degree Linear Polarization (DoLP) or Angle (AoLP). In this paper, we propose a demosaicking algorithm by inverting the formation m...

A simple but effective multiframe demosaicking method is proposed. Its primary goal is to replace more expensive mechanical motion compensation systems. Therefore, it is designed to be easily implemented in hardware for consumer devices. The described multiframe demosaicking algorithm is suitable for mass production devices such as mobile phones or digital cameras. It is compared to a multifram...

Finding a robust image prior is one of the fundamental challenges in image recovery problems. Many priors are based on the statistics of the noise source or assumed features (e.g. sparse gradients) of the image. More recently, priors based on convolutional neural networks have gained increased attention, due to the availability of training data and flexibility of a neural network-based prior. H...

1.1 Introduction Owing to the growing ubiquity of digital image acquisition and display, several factors must be considered when developing systems to meet future color image processing needs, including improved quality, increased throughput, and greater cost-effectiveness [1, 2, 3]. In consumer still-camera and video applications , color images are typically obtained via a spatial subsampling ...