Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages.  Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision

Voice Activity Detection using Group Delay Processing on Buffered Short-term Energy

In this paper, we present an algorithm for Voice Activity Detection (VAD) in speech signals using the minimum phase group delay function. The proposed method considers a buffer consisting of contiguous frames of the given signal and computes the short-term energy (STE) for that buffer. By appending a surrogate signal to STE and viewing the resultant signal as a positive part of the magnitude spectrum of an arbitrary signal, the minimum phase group delay function is computed. The group delay is then noise compensated and median filtered. The regions having positive group delay values are classified as speech and those with negative values are classified as noise. Experimental comparisons with the G.729 Annexe B VAD algorithm demonstrates significantly better performance for the proposed method, revealing that the algorithm is robust to noise. 1