No abstract

Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non‐CNN methods like block‐matching and three‐dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state‐of‐the‐art for years. This study provides a comprehensive study of state‐of‐the‐art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN‐S, DnCNN‐B, IDCNN), non‐locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN‐S and PDNN remove Gaussian noise of fixed level, whereas DnCNN‐B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD‐68 and Set‐12 datasets. PDNN shows the best result in terms of PSNR for both BSD‐68 and Set‐12 datasets.

Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise type is discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNNbased Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.

The knowledge discovery from large database is useful for decision making in industry real-time problems. Given a large voluminous transaction database, the knowledge is discovered by extracting maximal pattern after some analysis. Various methods have been proposed for extracting maximal pattern including FP and CP trees. It has been noticed that time taken by these methods for mining is found to be large. This paper modifies tree construction strategy of CP-tree for mining maximal pattern and the strategy takes less time for mining. The proposed modified CP-tree is constructed in two phases. The first phase constructs the tree based on user given item order along with its corresponding item list. In the second phase, each node in the branch of the constructed tree is dynamically rearranged based on item sorted list. The maximal patterns are retrieved from the proposed tree using the FPmax algorithm. The proposed tree has been built to support both interactive and incremental mining. The performance is evaluated using both dense and sparse bench mark data sets such as CHESS, MUSHROOM, CONNECT-4, PUMSB, and RETAIL respectively. The performance of the modified CP-tree is encouraging compared to some of the recently proposed approaches.

Convolutional neural networks (CNNs) based on the discriminative learning model have been widely used for image denoising. In this study, a feed-forward denoising CNN (DnCNN) with a parametric rectified linear unit (PReLU) is used to improve the denoising performance. PReLU enhances the model fitting of the DnCNN network without affecting computational cost. This network learns the leaky parameter of negative inputs in an activation function and therefore finds a proper slope in a negative direction. The proposed denoising network is based on residual learning, which comprises repeated convolutional and PReLU units along with batch normalisation. Residual learning with batch normalisation accelerates the network training, which can be used for blind Gaussian denoising. In this network, feature maps are processed by principal component analysis and transferred to subsequent convolution layers. An adaptive bilateral filter further processes the output image of the proposed CNN for image smoothening and sharpening. The mean and variance of the Gaussian kernel of adaptive filter vary from pixel to pixel. The performance of this network is analysed on BSD-68 and Set-12 datasets, and it exhibits an improvement in peak signal-to-noise ratio and structural similarity index metric and visual representation over other state-of-the-art methods.