Restoring a high-quality image from a noisy version poses a significant challenge in computer vision, particularly in today’s context where high-resolution and large-sized images are prevalent. As such, fast and efficient techniques are required to address noise reduction in such images effectively. Deep CNN-based image-denoising algorithms have gained popularity due to the rapid growth of deep learning and convolutional neural networks (CNNs). However, many existing deep learning models require paired clean/noisy images for training, limiting their utility in real-world denoising scenarios. In this paper, we propose a fast residual denoising framework (FRDF) designed based on zero-shot learning to address this issue. The FRDF first employs a novel downsampling technique to generate six different images from the noisy input, which are then fed into a lightweight residual network with 23K parameters. The network effectively utilizes a hybrid loss function, including residual, regularization, and guidance losses, to produce high-quality denoised images. Our innovative downsampling approach incorporates zero-shot learning principles, enabling our framework to generalize to unseen noise types and adapt to diverse noise conditions without the need for labelled data. Extensive experiments conducted on synthetic and real images confirm the superiority of our proposed approach over existing dataset-free methods. Extensive experiments conducted on synthetic and real images show that our method achieves up to 2 dB improvements in PSNR on the McMaster and Kodak24 datasets. This renders our approach applicable in scenarios with limited data availability and computational resources. Our code is available at [FRDF-code].

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning—among others—mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the $InceptionV3$ model, which achieved a score of 88.06%.

The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.