Bayer Demosaicking With Polynomial Interpolation

Abstract

Demosaicking is a digital image process to reconstruct full color digital images from incomplete color samples from an image sensor. It is an unavoidable process for many devices incorporating camera sensor (e.g., mobile phones, tablet, and so on). In this paper, we introduce a new demosaicking algorithm based on polynomial interpolation-based demosaicking. Our method makes three contributions: calculation of error predictors, edge classification based on color differences, and a refinement stage using a weighted sum strategy. Our new predictors are generated on the basis of on the polynomial interpolation, and can be used as a sound alternative to other predictors obtained by bilinear or Laplacian interpolation. In this paper, we show how our predictors can be combined according to the proposed edge classifier. After populating three color channels, a refinement stage is applied to enhance the image quality and reduce demosaicking artifacts. Our experimental results show that the proposed method substantially improves over the existing demosaicking methods in terms of objective performance (CPSNR, S-CIELAB ΔE*, and FSIM), and visual performance.

Low Cost Edge Sensing for High Quality Demosaicking

“…Measuring Variation To measure directional variation, the Hamilton-Adams (HA) method combines the first-and second-order differencing magnitude in two channels at the current single pixel [2]. This method is also adopted in subsequent works such as [6] and [14]. A more robust approach is to accumulate the directional differencing magnitude in a local neighbourhood (e.g., [15] [10] [5] [13]), or further at a mixture of scales (e.g., [16]).…”

“…In case of ties, interpolations in both directions are averaged [2]. Su extended this idea by fusing the horizontal and vertical interpolation using machine learned weights [17]; Chung-Chan selected the local dominant direction based on the variance of directional color differences [14]; In [18], local dominant direction is determined by voting for the horizontal or vertical or none edge hypotheses; Wu et al relaxed the strict prerequisite for none-edge judgment to approximate equality [6]. More methods estimate missing values by weighted summation of the estimation from the north, south, west and east directions, where the weights are obtained from the directional variation (e.g.…”

Bayer Image Demosaicking Using Eight-Directional Weights Based on the Gradient of Color Difference

“…However, when it deals with the edge of the image, it causes severe color artifacts. To gain a better interpolation performance along edges, some methods [2][3][4][5] exploit edge indicators to achieve edge-directed interpolation along the estimated interpolation directions, proving how important the edge indicator is in improving the quality of the interpolated image. Zhang and Wu [2] proposed a directional linear minimum mean square error (DLMMSE) estimation method, which adaptively estimated the missing pixel values by the linear minimum mean square error technique in two directions.…”

“…To improve the performance at the structural level [3], a nonlocal adaptive threshold was introduced. On the basis of polynomial interpolation, Wu et al came up with an alternative indicator and an edge classifier to enhance the interpolation accuracy [4]. Chen and Chang [5] proposed an accurate edge detecting method to minimize the color artifacts at the edges.…”

Rational Spline Image Upscaling with Constraint Parameters

“…As an important technique, it pervades many applications [1,2]. A digital image is not an exact snapshot of reality, it is only a discrete approximation.…”