On the Efficacy of Deep Image Denoising for Computer Vision Applications

Abstract

AbstractImage denoising is a process of inverse reconstruction where the original image is reconstructed from its noisy observations. Several deep learning models have been developed for image denoising. Usually, the performance of image denoising is measured by metrics like structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), however in this paper, we take a more pragmatic approach. We design and conduct experiments to evaluate the performance of deep image denoising methods in terms of improving the performance of some popular computer vision (CV) algorithms after image denoising. In this paper, we have comparatively analyzed: fast and flexible denoising (FFDNet) convolution neural network (CNN), feed forward denoising CNN (DnCNN), and deep image prior (DIP)-based image denoising. CV algorithms experimented with are face detection, face recognition, and object detection. Standard and augmented datasets were used in our experiments. Various types and amounts of noise were added to raw images from standard datasets (BSDS500, LFW, FDDB, and WGSID). We may conclude from our findings that image denoising does not improve the performance of CV algorithms when applied to raw images of datasets. But image denoising is very effective in improving the performance of the CV methods when denoising is applied to noise corrupted images of the datasets. In our experiments, we found results where the improvements were up to 11.70% in terms of accuracy for the face detection experiment.KeywordsImage denoisingComputer visionFace recognitionFace detectionObject detectionConvolution neural networkDeep learning