Protecting fine details and edges while denoising digital images is a challenging area of research due to changing characteristics of both, noise and signal. Denoising is used to remove noise from corrupted images but in the process fine details like weak edges and textures are hampered. In this paper we propose an algorithm based on Ridgelet transform to denoise images and protect fine details. Here we use cycle spinning on Ridgelet coefficients with soft thresholding and name the algorithm as Ridgelet Shrinkage in order to suppress noise and preserve details. The projections in Ridgelets filter out the noise while protecting the details while the ridgelet shrinkage further suppress noise. The proposed algorithm out performs the Wavelet Shrinkage and Non-local (NL) means denoising algorithms on the basis of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) numerically and visually both.
Proc. SPIE. 9445, Seventh International Conference on Machine Vision (ICMV 2014)
KEYWORDS: Image processing algorithms and systems, Genetic algorithms, Visualization, Image segmentation, Image processing, Color image processing, Human vision and color perception, Genetics, Color image segmentation, RGB color model
This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.