SVM-Based Switching Filter Hardware Design for Mixed Noise Reduction in Digital Images Using High-Level Synthesis Tools

Introduction

Image and video data processing has become ubiquitous in the recent years due to the popularity of smart surveillance, advance driver assistance, augmented reality and virtual reality etc. Digital Image Processing (DIP) is a broad field with operations ranging from image quality enhancement (e.g. contrast adjustment and denoising etc.), content analysis (e.g. frequency domain analysis and segmentation etc.) to implementation of compression techniques (e.g. Discrete Cosine and Wavelet Transforms etc.). Image denoising is a particularly important area in this regard since different types of noise arise in digital images due to imperfections in imaging systems, compression and communication channels etc. Conventionally, these denoising operations have been performed using mathematical operations (e.g. convolution) with fixed parameters (kernels). Such image processing filters are linear in nature and work well for most typical scenarios where imaging conditions are known and invariable (Gonzalez & Woods, 2006). Some non-linear operations (e.g. rank-order filtering, closure and dilation etc.) have also been advised for specific cases. For dynamically changing scenarios, adaptive filtering techniques (K.N. & A.N., 2000; Khan & Lee, 2017) have also been recommended in the past. Two common types of noise affecting digital images are impulse (Salt and Pepper) noise and Gaussian noise. Conventionally, these have been treated using Median and Wiener adaptive filters respectively. These filters are known for their edge preserving property. However, any filtering operation necessarily affects the sharp edges and resolution of the image. In order to quantify the distortion, two metrics have been widely used by the research community i.e. Signal to Noise Ratio (SNR) and Structural Similarity Index (SSIM) (Zhou, Bovik, Sheikh, & Simoncelli, 2004). Figure 1 shows an example where input images have been corrupted by synthetic noise i.e. 25% impulse noise (top) and Gaussian noise with σ=0.025 (bottom). These noisy images have been filtered through median and Wiener adaptive filters respectively. Resultantly, the SNR of the top noisy image has improved from 6.7 dB to 20.2 dB while the bottom image sees an increase of SNR from 10.1dB to 18.96 dB. Similarly, SSIM of the noisy images increase from 0.077 to 0.78 (top) and from 0.42 to 0.73 (bottom). Gaussian noise particularly affects the image texture more and hence the recovered images depict lower SNR and SSIM values.

Figure 1.

Image denoising examples using conventional approaches. Images on the left are the original images. Middle images have been corrupted by impulse (top) and Gaussian (bottom) noise respectively. Images on the right are the results of filtering by median (top) and Wiener adaptive (bottom) filters respectively.