Low light images are seriously corrupted by noise due to the low signal-to-noise ratio (SNR). In low intensity, just-noticeable-difference (JND) is high, and thus the noise is not perceived well by human eyes. However, after contrast enhancement the noise becomes obvious and severe because JND decreases as intensity increases. Thus, contrast enhancement without considering human visual perception causes serious noise amplification in low light images. In this paper, we propose perceptual enhancement of low light images based on two-step noise suppression. We adopt two-step noise suppression based on noise characteristics corresponding to human visual perception. First, we perform noise aware contrast enhancement using a noise level function (NLF). However, the increase of the intensity caused by contrast enhancement reduces JND in low intensity, which makes noise much visible by human eyes. Second, we perceptually reduce noise in images while preserving details using a JND model which represents noise visibility in contrast enhancement. We estimate the noise visibility based on the intensity change using luminance adaptation. Also, we extract image details by contrast masking and visual regularity because textural regions have higher visibility thresholds than smooth ones. Based on the human visual characteristics, we perform perceptual noise suppression using the JND model. Experimental results show that the proposed method perceptually enhances contrast in low light images while successfully minimizing distortions and preserving details.

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception. First, we generate a JND map based on an the human visual system (HVS) response model from foreground and background luminance, called JND transform. Second, for base image, we perform perceptual contrast enhancement based on luminance adaptation to effectively allocate a dynamic range to each gray level while preventing under enhancement (tone distortion) and over-enhancement. Third, we refine the JND map using Weber's law, luminance adaptation and visual masking. Weber's law enhances the JND map based on the luminance variation after contrast enhancement. Luminance adaptation suppresses noise for smooth regions, while visual masking enforces detail enhancement for textural regions. Fourth, we perform inverse JND transform to generate the enhanced luma channel from the JND map and base image. Finally, we conduct chroma denoising by transferring texture information of the enhanced luma channel to the chroma channels with guided filtering. Experimental results show that the proposed method achieves both contrast enhancement and noise reduction for low light images as well as outperforms state-of-the-art methods in terms of quantitative measurements.

In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise. Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF). NLF is used to get a noise aware histogram which prevents noise amplification, and we use the noise aware histogram in contrast enhancement. However, the increase of intensity by contrast enhancement reduces the visibility threshold, which makes noise visible by human eyes. Second, we utilize a just noticeable difference (JND) model from luminance adaptation to suppress noise based on human visual perception. Experimental results show that the proposed method successfully enhances contrast in low light images while minimizing noise amplification.

Infrared and visible image fusion is currently an important research direction in the field of multimodal image fusion, which aims to utilize the complementary information between infrared images and visible images to generate a new image containing richer information. In recent years, many deep learning-based methods for infrared and visible image fusion have emerged. However, most of these approaches ignore the importance of semantic information in image fusion, resulting in the generation of fused images that do not perform well enough in human visual perception and advanced visual tasks. To address this problem, we propose a semantic prior knowledge-driven infrared and visible image fusion method. The method utilizes a pre-trained semantic segmentation model to acquire semantic information of infrared and visible images, and drives the fusion process of infrared and visible images through semantic feature perception module and semantic feature embedding module. Meanwhile, we divide the fused image into each category block and consider them as components, and utilize the regional semantic adversarial loss to enhance the adversarial network generation ability in different regions, thus improving the quality of the fused image. Through extensive experiments on widely used datasets, the results show that our approach outperforms current leading algorithms in both human eye visualization and advanced visual tasks. The source code is available at https://github.com/tianzhiya/SPDFusion.

In this paper, we propose perceptually optimized enhancement of contrast and color in images using just-noticeabledifference (JND) transform and color constancy. We adopt JND transform to get JND map that represents the perceptual response of the human visual system (HVS). We utilize color constancy to estimate the light source color and be robust to color bias. First, we use a perceptual generalized equalization model (GEM) for the optimization of both color and contrast based on color constancy and contrast enhancement, i.e. base image. Second, we generate JND map based on HVS response model from foreground and background luminance, called JND transform. Next, we update the JND map based on Weber’s law to boost perceptual response. Finally, we perform inverse JND transform from the base image and its JND map to produce the enhanced image highly correlated with the human visual perception. Experimental results show that the proposed method achieves good performance in contrast enhancement, color reproduction and detail enhancement.

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.