Humans have always seen the world in color. In the last three decades, there has been rapid and enormous transition from grayscale images to color ones. Well-known objective evaluation algorithms for measuring image quality include mean squared error (MSE), peak signal-to-noise ratio (PSNR), and human Visual System based one are structural similarity measures and edge based similarity measures. One of the common and major limitations of these objective measures is that they evaluate the quality of grayscale images only and don't make use of image color information. Since, Color is a powerful descriptor that often simplifies the object identification and extraction from a scene so color information also could influence human beings' judgments. So, in this paper new objective color image quality measure in spatial domain is proposed that overcomes the limitation of these existing methods significantly, is easy to calculate and applicable to various image processing applications. The proposed quality measure has been designed as a combination of four main factors: luminance similarity, structure correlation, edge similarity, and color similarity. This proposed index is mathematically defined and in it HVS model is explicitly employed. Experiments on various image distortion types indicate that this index performs significantly better than other traditional error summation methods and existing similarity measures.

No abstract

Partial differential equation based anisotropic diffusion techniques are used extensively in computer vision for image enhancement and de-noising. Anisotropic diffusion is found to be an efficient and low computational complexity approach that has overcome the undesirable effects of linear smoothing filters and now is popular in prominent research areas of enhancing the quality of low contrast images and speckle noise reduction from geological, industrial, and medical images. This paper presents state-of-theart anisotropic diffusion technique and a comprehensive survey on various advancements in anisotropic diffusion for image enhancement and de-noising. The capability of anisotropic diffusion for enhancing the quality of low contrast images and speckle noise reduction from medical and industrial images are further explored. Various quality measures used to validate the performance are studied. The major research issues and possible future scopes in anisotropic diffusion filtering are also discussed. Povzetek: Prispevek predstavi pregled in novo metodo na področju anizotropnedifuzije za povečevanje slik in zmanjševanje šuma.

No abstract

No abstract