In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.

Image denoising is one of the hottest topics in image restoration area, it has achieved great progress both in terms of quantity and quality in recent years, especially after the wide and intensive application of deep neural networks. In many deep learning based image denoising models, the performance can greatly benefit from the prepared clean/noisy image pairs used for model training, however, it also limits the application of these models in real denoising scenes. Therefore, more and more researchers tend to develop models that can be learned without image pairs, namely the denoising models that can be well generalised in real‐world denoising tasks. This motivates to make a survey on the recent development of image denoising methods. In this paper, the typical denoising methods from the perspective of model training are reviewed, the reviewed methods are categorised into four classes: the models need clean/noisy image pairs to train, the models trained on multiple noisy images, the models can be learned from a single noisy image, and the visual transformer based models. The denoising results of different denoisers were compared on some public datasets to discover the performance and advantages. The challenges and future directions in image denoising area are also discussed.

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.