RETINAL VESSEL DETECTION USING SELF-MATCHED FILTERING

ABSTRACT Automated analysis of retinal images usually requires estimating the positions of blood vessels, which contain important features for image alignment and abnormality detection. Matched filtering can produce the best results but is difficult to implement because the vessel orientations and widths are unknown beforehand. Many researchers use Hessian filtering, which provides an estimate for vessel orientation through the use of three orientation templates. We propose a novel filtering approach, called self-matched filtering, which is based on the 180 • rotated version of the noisy vessel signal in the local neighborhood. We show that even though the proposed filter achieves half the signal-to-noise ratio of a matched filter, it does not require the estimation of the vessel scale and orientation, and can outperform Hessian filtering by up to a factor of two in terms of miss detection error

Edge-Preserving Image Denoising via Optimal Color Space Projection

Denoising of color images can be done on each color component independently. Recent work has shown that exploiting strong correlation between high-frequency content of different color components can improve the denoising performance. We show that for typical color images high correlation also means similarity, and propose to capture inter-color dependency using an optimal luminance/color-difference space projection. Experimental results confirm that performing denoising on the the projected color components yields superior to existing solutions denoising performance, both in PSNR and visual quality sense. We also develop a novel approach to estimate directly from the noisy image data the image and noise statistics, which are required to determine the optimal projection. Index Terms – Image denoising, wavelet thresholding, luminance, color differences, optimal color projectio

Color Image Denoising Using Wavelets and Minimum Cut Analysis

Abstract—Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images, especially when it exploits the interscale correlations of wavelet coefficients. Intrascale correlations can further improve the denoising performance, but the gain for grayscale images is generally small. In this letter, we demonstrate that the gain can become substantial in color image denoising, especially for smooth image color-difference components. We then propose a new denoising method, based on the minimum cut algorithm, to exploit both the interscale and intrascale correlations of wavelet coefficients. The proposed method achieves up to 5-dB gain in peak signal-to-noise ratio for color-difference images and leads to fewer visual color artifacts. Index Terms—Color difference, color image denoising, minimum cut, wavelet. I

Reversing Demosaicking and Compression in Color Filter Array Image Processing: Performance Analysis and Modeling

Abstract—In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios. To provide a theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and provide better understanding of DSC processing chains. The modeling also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for future development in this area. Index Terms—Color filter array (CFA), color transformation, compression, demosaicking, error model, image processing chain. I