Exploring Digital Image Dithering Techniques on a Broken Foot Image

The contouring effects appear when an image is quantized rudely irrespective of the uniform or non-uniform quantization. To mitigate the effects of contouring, a small amount of random noise is added (dithered) to the original image before quantization. Techniques such as dithering and half-toning are widely used strategies in obtaining images and texts in magazines, newspapers, books, printers, computer monitors, and LCDs. This study explores the dithering technique on a broken foot image with more elaborative methods and results. All the experiments involved in this study, such as quantization, dithering, no dithering, and dithering, quantized, and filtered techniques, are conducted using the Matlab R2016b tool. Overall information and details are retained with the aid of lowpass filtering and highpass filtering, respectively. Simulation results such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are obtained in every stage of the dithering procedure to analyze and compare the performance or accuracy.

Acquisition of the Palm Print and Research on Pretreating Simulation

The paper does the pretreating operation of palm print, the process of which includes lowpass filtering, histogram equalization, binarization processing, edge extraction, corner detection, rotating location and extraction of subgraph ROI. In images done with pretreating, translation and rotation phenomenon of noise level has almost been removed, which benefits the characteristic extraction and identification of palm print. And we finally demonstrate by experiments.

Image Denoising via 2-D FIR Filtering Approach

Image denoising has received much concern for decades. One of the simplest methods for image denoising is the 2-D FIR lowpass filtering approach. Firstly, the authors make a comparative study of the conventional lowpass filtering approach, including the classical mean filter and three 2-D FIR LowPass Filters (LPF) designed by McClellan transform. Then an improved method based on learning method is presented, where pixels are filtered by five edge-oriented filters, respectively, facilitated to their edge details. Differential Evolution Particle Swarm Optimization (DEPSO) algorithm is exploited to refine those filters. Computer simulation demonstrates that the proposed method can be superior to the conventional filtering method, as well as the modern Bilateral Filtering (BF) and the Stochastic Denoising (SD) method.