Images captured under low-light conditions are typically prone to undesirable visual phenomena, particularly color distortion and additive noise, that impede the aesthetics and performance of high-level vision tasks. Therefore, developing an enhancement method is necessary to obtain visually pleasing images. This paper proposes deep multiple networks for low-light image enhancement based on the Retinex model (defined as DMT-Net), wherein each network is configured to perform its own role in the YCbCr color space. More specifically, Decoupled-Net is presented to accurately decouple the luminance channel into reflectance and illumination components using the Retinex model. Denoising-Net is connected to Decoupled-Net to eradicate additive noise in the reflectance component. Boosting-Net enhances the illumination component and reduces halo artifacts. In addition, we propose Chrominance- Net to mitigate the distortion of the Cb and Cr channels. Our study also focuses on having a suitable norm order for the loss function based on kurtosis characteristics, such that each network is properly trained to minimize the residual in the training process. Finally, the experimental results prove that our proposed method outperforms numerous other cutting-edge methods in both qualitative and quantitative terms.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.

Low-light images are susceptible to poor visibility, low contrast, and additive noise. Although various techniques can enhance brightness to a reasonable extent, they inevitably amplify noise and cause color distortion, halo artifacts, and other forms of deterioration. Most low-light image enhancement (LLIE) methods utilize multiple algorithms or models to remove the corresponding deterioration types while brightening the image. However, an optimization strategy can be difficult, primarily because each sub-model has its own set of hyperparameters that must be tuned. To address this problem, we propose an end-to-end network, called CBMD-Net, which concurrently brightens and mitigates the deterioration in low-light images. Specifically, our network is developed on a U-shaped backbone with several stacked pyramid depthwise separable convolution (PDC) modules, which accurately extract meaningful spatial features from the network. Additionally, a new upsampling module is presented to efficiently restore the spatial resolution of the feature maps. Finally, we discovered that the latent representations at the network bottleneck contain relevant information regarding the input image, including deteriorated features that can have repercussions on image quality. Therefore, we introduce an intermediate loss function at the bottleneck to mitigate the additive noise and negative effects. Comprehensive experiments demonstrate that the developed network can obtain satisfactory results from both subjective and objective comparisons against state-of-the-art methods.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.