Research On Color Interpolation And Noise Reduction Algorithms Based On Bayer Pattern Digital Image Sensors

This dissertation researches color interpolation and noise reduction algorithms based on Bayer pattern digital image sensors. For size and cost considerations, most digital cameras use a single image sensor, whose surface is covered with color filter array, each pixel sample of collected image only has one color component. This dissertation researches algorithms based on Bayer pattern filter array which is the most popular at present. To render a full color image, two missing colors are needed to be estimated from surrounding samples, referred to as color interpolation. In addition, white noise and impulse noise which are the most likely to exist have an obvious impact on imaging quality. This dissertation deals with noise reduction before color interpolation in the imaging process to make the original image noise model not destroyed by color interpolation.For the common white noise and impulse noise existing in digital cameras, according to color components spatial characteristics of Bayer pattern raw data, this dissertation removes impulse noise first, and then reduces white noise with Epsilon-Filter to smooth the image flat region, preserve image edge and enhance contrast around edges. According to simulation with different Bayer pattern original images, we have verified the proposed noise reduction algorithms, and the peak signal-to-noise ratio is computed between the original and denoised image in the whole region.This dissertation reviews some commonly used color interpolation algorithms and then proposes an improved color interpolation algorithm which uses the combination of green component and red & blue components step interpolation to reconstruct color components. The first step is computing green components with adapted color plane interpolation algorithm at red and blue samples, the second step is calculating the differences between red & green component and blue & green component, the third step is rendering full-resolution color difference image with the compensation of reconstructed green component and edge adaptive, and the last step is reconstructing red and blue components. According to simulation with denoised testing images, the peak signal-to-noise ratio and computational complexity are computed, giving out data and visual comparisons directly, analyzing different performances between improved algorithm and commonly used algorithms.