Image denoising is a classical preprocessing stage used to enhance images. However, it is well known that there are many practical cases where different image denoising methods produce images with inappropriate visual quality, which makes an application of image denoising useless. Because of this, it is desirable to detect such cases in advance and decide how expedient is image denoising (filtering). This problem for the case of wellknown BM3D denoiser is analyzed in this paper. We propose an algorithm of decision-making on image denoising expedience for images corrupted by additive white Gaussian noise (AWGN). An algorithm of prediction of subjective image visual quality scores for denoised images using a trained artificial neural network is proposed as well. It is shown that this prediction is fast and accurate.
The evolution of modern sensors for image acquisition brings as much obstacles as many possibilities to obtain multidimensional data with high resolution and rich information. One of the most perceptible destructive factors in visual data is noise. Due to complexity of modern sensors and approaches to signal collecting or preprocessing, noise model becomes complicated. The article’s goal is to introduce and solve a problem of suppressing additive spatially correlated noise (ASCN) which is present in images due to different sources and has various levels of correlation. It is shown that even modern filters attempting to suppress correlated noise often demonstrate unsatisfactory efficiency. Here we propose and analyze two modifications of 2D discrete cosine transform (DCT) based filter and the state-of-the-art BM3D technique. Both are based on accounting spatial spectrum of the noise by setting frequency-dependent thresholds. Furthermore, the modified BM3D filter exploits a similarity measure robust to noise spectrum in block matching. c 2018 Society for Imaging Science and Technology.
We introduce a content-adaptive approach to image denoising where the filter design is based on mean opinion scores (MOSs) from preliminary experiments with volunteers who evaluated the quality of denoised image fragments. This allows to tune the filter parameters so to improve the perceptual quality of the output image, implicitly accounting for the peculiarities of the human visual system (HVS). A modification of the BM3D image denoising filter (Dabov et al., IEEE TIP, 2007), namely BM3DHVS, is proposed based on this framework. We show that it yields a higher visual quality than the conventional BM3D. Further, we have also analyzed the MOSs against popular full-reference visual quality metrics such as SSIM (Wang et al., IEEE TIP, 2004), its extension FSIM (Zhang et al., IEEE TIP, 2011), and the noreference IL-NIQE (Zhang et al., IEEE TIP, 2015) over each image fragment. Both the Spearman and the Kendall rank order correlation show that these metrics do not correspond well to the human perception. This calls for new visual quality metrics tailored for the benchmarking and optimization of image denoising methods.