Kim, Hyung Cook. Ph.D., Purdue University, May, 2006. Watermark and Data Hiding Evaluation: The Development of a Statistical Analysis Framework. Major Professor: Edward J. Delp. Digital watermarking is the practice of hiding a message in an image, audio, video or other digital media elements. Since the late 1990’s, there has been an explosion in the number of digital watermarking algorithms pub...

The assignment of a quantitative spectral image quality metric is discussed with an approach proposed. Quality is divided into fidelity and utility components, with utility studied in the context of object detection in hyperspectral imagery. ©2010 Optical Society of America OCIS codes: (110.3000) Image quality assessment; (110.4234) Multispectral and hyperspectral imaging; (280.4788) Optical se...

Benefiting from the strong capabilities of deep CNNs for feature representation and nonlinear mapping, deep-learning-based methods have achieved excellent performance in single image super-resolution. However, most existing SR depend on high capacity networks which is initially designed visual recognition, rarely consider initial intention super-resolution detail fidelity. Aiming at pursuing th...

Water making is the process of embedding data called a watermark into a multimedia object such that watermark can be detected or extracted later to make an assertion about the object. Earlier techniques based on spatial and frequency domain had the problems of a poor fidelity especially with higher payloads of watermark image. Unsatisfactory value of Peak Signal to Noise Ratio (PSNR) of waterma...

The wavelet based SPIHT algorithm has recently attracted significant attentions from researchers in image coding. SPIHT maximizes PSNR (or minimize MSE) for each bit it transmitted, however, it is well known that PSNR is not a good indicator of the perceptual image fidelity. Human visual perception is more sensitive to distortions in lower frequency components than distortions in the higher fre...

We consider signal and image restoration using convex cost-functions composed of a non-smooth data-fidelity term and a smooth regularization term. First, we provide a convergent method to minimize such cost-functions. Then we propose an efficient method to remove impulsive noise by minimizing cost-functions composed of an l1 data-fidelity term and an edge-preserving regularization term. Their m...